# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline
#import os
import tensorflow as tf
print(plt.get_backend())
module://ipykernel.pylab.backend_inline
homeb = pd.read_csv("../nilm_metadata/Smart_microgrid/House wise data/HomeB/2016/HomeB-meter1_2016.csv", index_col=0)
homeb_shape=homeb.shape
homeb_type=type(homeb)
print ("shape : "+str(homeb_shape) +" and type : " + str(homeb_type))
print (homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59","Washer [kW]"].describe())
shape : (247600, 17) and type : <class 'pandas.core.frame.DataFrame'> count 48.000000 mean 0.008688 std 0.021093 min 0.003182 25% 0.003241 50% 0.003357 75% 0.003832 max 0.127577 Name: Washer [kW], dtype: float64
homeb.info
<bound method DataFrame.info of use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
Date & Time
2016-01-01 00:00:00 0.455814 0.0 0.455814 0.001166 0.189853
2016-01-01 00:30:00 0.403376 0.0 0.403376 0.000659 0.104036
2016-01-01 01:00:00 0.662962 0.0 0.662962 0.000823 0.107150
2016-01-01 01:30:00 0.677851 0.0 0.677851 0.001426 0.196811
2016-01-01 02:00:00 0.425556 0.0 0.425556 0.000759 0.119401
2016-01-01 02:30:00 0.448744 0.0 0.448744 0.000652 0.124341
2016-01-01 03:00:00 0.751886 0.0 0.751886 0.001643 0.325440
2016-01-01 03:30:00 0.554862 0.0 0.554862 0.001001 0.195691
2016-01-01 04:00:00 0.481422 0.0 0.481422 0.000929 0.183667
2016-01-01 04:30:00 0.640826 0.0 0.640826 0.002342 0.434424
2016-01-01 05:00:00 0.491982 0.0 0.491982 0.001082 0.211639
2016-01-01 05:30:00 0.450740 0.0 0.450740 0.000891 0.173173
2016-01-01 06:00:00 0.390099 0.0 0.390099 0.001082 0.202955
2016-01-01 06:30:00 0.477034 0.0 0.477034 0.001015 0.196979
2016-01-01 07:00:00 0.464292 0.0 0.464292 0.001019 0.195085
2016-01-01 07:30:00 0.421597 0.0 0.421597 0.001191 0.210581
2016-01-01 08:00:00 0.520880 0.0 0.520880 0.001125 0.206050
2016-01-01 08:30:00 0.280367 0.0 0.280367 0.000067 0.010183
2016-01-01 09:00:00 0.280385 0.0 0.280385 0.000074 0.009666
2016-01-01 09:30:00 0.397311 0.0 0.397311 0.000066 0.009701
2016-01-01 10:00:00 0.453370 0.0 0.453370 0.000155 0.009593
2016-01-01 10:30:00 0.432927 0.0 0.432927 0.000079 0.009718
2016-01-01 11:00:00 0.332945 0.0 0.332945 0.000036 0.009689
2016-01-01 11:30:00 0.197093 0.0 0.197093 0.000058 0.009607
2016-01-01 12:00:00 0.212063 0.0 0.212063 0.000057 0.009541
2016-01-01 12:30:00 0.305679 0.0 0.305679 0.000038 0.009622
2016-01-01 13:00:00 0.193844 0.0 0.193844 0.000061 0.009536
2016-01-01 13:30:00 0.201886 0.0 0.201886 0.000058 0.009632
2016-01-01 14:00:00 0.579818 0.0 0.579818 0.000842 0.009392
2016-01-01 14:30:00 1.846798 0.0 1.846798 0.007136 0.202987
... ... ... ... ... ...
2016-12-31 23:30:00 0.280300 0.0 0.280300 0.000300 0.008817
2016-12-31 23:31:00 0.279600 0.0 0.279600 0.000283 0.008800
2016-12-31 23:32:00 0.279767 0.0 0.279767 0.000283 0.008817
2016-12-31 23:33:00 0.280400 0.0 0.280400 0.000300 0.008817
2016-12-31 23:34:00 0.280267 0.0 0.280267 0.000283 0.008817
2016-12-31 23:35:00 0.294933 0.0 0.294933 0.000367 0.023333
2016-12-31 23:36:00 0.280567 0.0 0.280567 0.000300 0.008817
2016-12-31 23:37:00 0.280233 0.0 0.280233 0.000300 0.008817
2016-12-31 23:38:00 0.280700 0.0 0.280700 0.000283 0.008817
2016-12-31 23:39:00 0.280400 0.0 0.280400 0.000283 0.008833
2016-12-31 23:40:00 0.280400 0.0 0.280400 0.000300 0.008800
2016-12-31 23:41:00 0.279817 0.0 0.279817 0.000300 0.008817
2016-12-31 23:42:00 0.280150 0.0 0.280150 0.000283 0.008833
2016-12-31 23:43:00 0.280800 0.0 0.280800 0.000300 0.008867
2016-12-31 23:44:00 0.280633 0.0 0.280633 0.000300 0.008833
2016-12-31 23:45:00 0.281833 0.0 0.281833 0.000283 0.008900
2016-12-31 23:46:00 0.281267 0.0 0.281267 0.000300 0.008917
2016-12-31 23:47:00 0.280867 0.0 0.280867 0.000300 0.008900
2016-12-31 23:48:00 0.281117 0.0 0.281117 0.000283 0.008900
2016-12-31 23:49:00 0.329833 0.0 0.329833 0.000283 0.008917
2016-12-31 23:50:00 0.424533 0.0 0.424533 0.000267 0.008900
2016-12-31 23:51:00 0.409067 0.0 0.409067 0.000267 0.008933
2016-12-31 23:52:00 0.408533 0.0 0.408533 0.000267 0.008917
2016-12-31 23:53:00 0.407117 0.0 0.407117 0.000267 0.008917
2016-12-31 23:54:00 0.407150 0.0 0.407150 0.000267 0.008933
2016-12-31 23:55:00 0.406400 0.0 0.406400 0.000267 0.008950
2016-12-31 23:56:00 0.405350 0.0 0.405350 0.000267 0.008950
2016-12-31 23:57:00 0.404333 0.0 0.404333 0.000267 0.008900
2016-12-31 23:58:00 0.403883 0.0 0.403883 0.000267 0.008900
2016-12-31 23:59:00 0.382550 0.0 0.382550 0.000200 0.008883
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
Date & Time
2016-01-01 00:00:00 0.006457 0.003416 0.032004
2016-01-01 00:30:00 0.006415 0.003429 0.031977
2016-01-01 01:00:00 0.006254 0.003527 0.119877
2016-01-01 01:30:00 0.006257 0.003562 0.132939
2016-01-01 02:00:00 0.006401 0.003441 0.054034
2016-01-01 02:30:00 0.006592 0.003307 0.003767
2016-01-01 03:00:00 0.006718 0.003231 0.003629
2016-01-01 03:30:00 0.006653 0.003261 0.003701
2016-01-01 04:00:00 0.006651 0.003241 0.003699
2016-01-01 04:30:00 0.021909 0.003182 0.003640
2016-01-01 05:00:00 0.006612 0.003232 0.003672
2016-01-01 05:30:00 0.006599 0.003240 0.003694
2016-01-01 06:00:00 0.006612 0.003255 0.003724
2016-01-01 06:30:00 0.006719 0.003241 0.003690
2016-01-01 07:00:00 0.006712 0.003234 0.013461
2016-01-01 07:30:00 0.006651 0.003297 0.031757
2016-01-01 08:00:00 0.006836 0.003274 0.031877
2016-01-01 08:30:00 0.006673 0.003328 0.031785
2016-01-01 09:00:00 0.006619 0.003317 0.031693
2016-01-01 09:30:00 0.015209 0.003297 0.031712
2016-01-01 10:00:00 0.059444 0.003314 0.031705
2016-01-01 10:30:00 0.030112 0.003232 0.022193
2016-01-01 11:00:00 0.006735 0.003198 0.003744
2016-01-01 11:30:00 0.006667 0.003224 0.003782
2016-01-01 12:00:00 0.006624 0.003207 0.003752
2016-01-01 12:30:00 0.006697 0.003197 0.003714
2016-01-01 13:00:00 0.006616 0.003229 0.003737
2016-01-01 13:30:00 0.006681 0.003225 0.003762
2016-01-01 14:00:00 0.006339 0.003814 0.003987
2016-01-01 14:30:00 0.004072 0.007419 0.005669
... ... ... ...
2016-12-31 23:30:00 0.005800 0.003450 0.051433
2016-12-31 23:31:00 0.005783 0.003467 0.051450
2016-12-31 23:32:00 0.005817 0.003467 0.051483
2016-12-31 23:33:00 0.005800 0.003450 0.051450
2016-12-31 23:34:00 0.005817 0.003467 0.051467
2016-12-31 23:35:00 0.005817 0.003450 0.051517
2016-12-31 23:36:00 0.005817 0.003467 0.051533
2016-12-31 23:37:00 0.005817 0.003450 0.051500
2016-12-31 23:38:00 0.005833 0.003450 0.051517
2016-12-31 23:39:00 0.005817 0.003450 0.051533
2016-12-31 23:40:00 0.005817 0.003450 0.051450
2016-12-31 23:41:00 0.005800 0.003450 0.051483
2016-12-31 23:42:00 0.005817 0.003467 0.051533
2016-12-31 23:43:00 0.005833 0.003450 0.051550
2016-12-31 23:44:00 0.005817 0.003467 0.051500
2016-12-31 23:45:00 0.005817 0.003467 0.051500
2016-12-31 23:46:00 0.005817 0.003467 0.051533
2016-12-31 23:47:00 0.005833 0.003450 0.051517
2016-12-31 23:48:00 0.005817 0.003467 0.051533
2016-12-31 23:49:00 0.005850 0.003450 0.051533
2016-12-31 23:50:00 0.005883 0.003433 0.051483
2016-12-31 23:51:00 0.005867 0.003433 0.051500
2016-12-31 23:52:00 0.005867 0.003433 0.051483
2016-12-31 23:53:00 0.005883 0.003433 0.051450
2016-12-31 23:54:00 0.005883 0.003433 0.051517
2016-12-31 23:55:00 0.005900 0.003450 0.051567
2016-12-31 23:56:00 0.005883 0.003433 0.051533
2016-12-31 23:57:00 0.005900 0.003433 0.051517
2016-12-31 23:58:00 0.005900 0.003450 0.051517
2016-12-31 23:59:00 0.005917 0.003367 0.051400
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
Date & Time
2016-01-01 00:00:00 0.002309 0.004976
2016-01-01 00:30:00 0.002378 0.004958
2016-01-01 01:00:00 0.002413 0.012929
2016-01-01 01:30:00 0.002367 0.004988
2016-01-01 02:00:00 0.002423 0.005025
2016-01-01 02:30:00 0.002330 0.004991
2016-01-01 03:00:00 0.002167 0.005024
2016-01-01 03:30:00 0.002261 0.005003
2016-01-01 04:00:00 0.002260 0.004975
2016-01-01 04:30:00 0.002108 0.005061
2016-01-01 05:00:00 0.002238 0.004973
2016-01-01 05:30:00 0.002237 0.004929
2016-01-01 06:00:00 0.002234 0.004961
2016-01-01 06:30:00 0.002256 0.005004
2016-01-01 07:00:00 0.002256 0.004957
2016-01-01 07:30:00 0.002279 0.004996
2016-01-01 08:00:00 0.002298 0.005044
2016-01-01 08:30:00 0.002438 0.004976
2016-01-01 09:00:00 0.002424 0.004939
2016-01-01 09:30:00 0.002433 0.004939
2016-01-01 10:00:00 0.002535 0.004947
2016-01-01 10:30:00 0.002444 0.018252
2016-01-01 11:00:00 0.002355 0.004806
2016-01-01 11:30:00 0.002377 0.004812
2016-01-01 12:00:00 0.002354 0.004758
2016-01-01 12:30:00 0.002362 0.004798
2016-01-01 13:00:00 0.002402 0.004859
2016-01-01 13:30:00 0.002399 0.004854
2016-01-01 14:00:00 0.002533 0.012481
2016-01-01 14:30:00 0.003334 0.005259
... ... ...
2016-12-31 23:30:00 0.003333 0.004883
2016-12-31 23:31:00 0.003333 0.004917
2016-12-31 23:32:00 0.003350 0.004933
2016-12-31 23:33:00 0.003333 0.004917
2016-12-31 23:34:00 0.003350 0.004917
2016-12-31 23:35:00 0.003333 0.004900
2016-12-31 23:36:00 0.003350 0.004917
2016-12-31 23:37:00 0.003333 0.004917
2016-12-31 23:38:00 0.003333 0.004883
2016-12-31 23:39:00 0.003333 0.004917
2016-12-31 23:40:00 0.003333 0.004900
2016-12-31 23:41:00 0.003333 0.004883
2016-12-31 23:42:00 0.003317 0.004900
2016-12-31 23:43:00 0.003333 0.004900
2016-12-31 23:44:00 0.003333 0.004900
2016-12-31 23:45:00 0.003333 0.004900
2016-12-31 23:46:00 0.003350 0.004917
2016-12-31 23:47:00 0.003333 0.004933
2016-12-31 23:48:00 0.003350 0.004933
2016-12-31 23:49:00 0.003350 0.004950
2016-12-31 23:50:00 0.003350 0.004933
2016-12-31 23:51:00 0.003333 0.004967
2016-12-31 23:52:00 0.003350 0.004967
2016-12-31 23:53:00 0.003333 0.004933
2016-12-31 23:54:00 0.003333 0.004950
2016-12-31 23:55:00 0.003333 0.004950
2016-12-31 23:56:00 0.003350 0.004967
2016-12-31 23:57:00 0.003350 0.004967
2016-12-31 23:58:00 0.003350 0.004967
2016-12-31 23:59:00 0.003333 0.004967
MBed + KBed outlets [kW] Dryer + egauge [kW] \
Date & Time
2016-01-01 00:00:00 0.090664 0.000021
2016-01-01 00:30:00 0.090591 0.000068
2016-01-01 01:00:00 0.090467 0.000173
2016-01-01 01:30:00 0.090688 0.000123
2016-01-01 02:00:00 0.077748 0.000040
2016-01-01 02:30:00 0.068908 0.000068
2016-01-01 03:00:00 0.068744 0.000064
2016-01-01 03:30:00 0.063393 0.000081
2016-01-01 04:00:00 0.056010 0.000059
2016-01-01 04:30:00 0.055681 0.000080
2016-01-01 05:00:00 0.055700 0.000038
2016-01-01 05:30:00 0.055679 0.000054
2016-01-01 06:00:00 0.055396 0.000012
2016-01-01 06:30:00 0.055600 0.000050
2016-01-01 07:00:00 0.055504 0.000039
2016-01-01 07:30:00 0.055432 0.000007
2016-01-01 08:00:00 0.055806 0.000056
2016-01-01 08:30:00 0.055760 0.000089
2016-01-01 09:00:00 0.055729 0.000062
2016-01-01 09:30:00 0.055844 0.000131
2016-01-01 10:00:00 0.063675 0.000129
2016-01-01 10:30:00 0.059654 0.000113
2016-01-01 11:00:00 0.055676 0.000128
2016-01-01 11:30:00 0.055546 0.000052
2016-01-01 12:00:00 0.055494 0.000064
2016-01-01 12:30:00 0.055548 0.000127
2016-01-01 13:00:00 0.055446 0.000048
2016-01-01 13:30:00 0.055501 0.000056
2016-01-01 14:00:00 0.278134 0.000270
2016-01-01 14:30:00 1.526282 0.000881
... ... ...
2016-12-31 23:30:00 0.089400 0.000100
2016-12-31 23:31:00 0.089467 0.000067
2016-12-31 23:32:00 0.089350 0.000133
2016-12-31 23:33:00 0.090067 0.000167
2016-12-31 23:34:00 0.090000 0.000067
2016-12-31 23:35:00 0.090050 0.000100
2016-12-31 23:36:00 0.090067 0.000100
2016-12-31 23:37:00 0.090067 0.000133
2016-12-31 23:38:00 0.090333 0.000133
2016-12-31 23:39:00 0.090067 0.000100
2016-12-31 23:40:00 0.090083 0.000100
2016-12-31 23:41:00 0.090017 0.000133
2016-12-31 23:42:00 0.090167 0.000067
2016-12-31 23:43:00 0.090583 0.000133
2016-12-31 23:44:00 0.090600 0.000100
2016-12-31 23:45:00 0.090917 0.000100
2016-12-31 23:46:00 0.090933 0.000100
2016-12-31 23:47:00 0.090733 0.000100
2016-12-31 23:48:00 0.090667 0.000067
2016-12-31 23:49:00 0.090617 0.000133
2016-12-31 23:50:00 0.090683 0.000200
2016-12-31 23:51:00 0.090617 0.000200
2016-12-31 23:52:00 0.090783 0.000200
2016-12-31 23:53:00 0.090750 0.000233
2016-12-31 23:54:00 0.090700 0.000233
2016-12-31 23:55:00 0.090567 0.000200
2016-12-31 23:56:00 0.090017 0.000200
2016-12-31 23:57:00 0.089817 0.000200
2016-12-31 23:58:00 0.089850 0.000200
2016-12-31 23:59:00 0.068817 0.000167
Panel GFI (central vac) [kW] Home Office (R) [kW] \
Date & Time
2016-01-01 00:00:00 0.000347 0.039824
2016-01-01 00:30:00 0.000353 0.039098
2016-01-01 01:00:00 0.000429 0.038571
2016-01-01 01:30:00 0.000429 0.039092
2016-01-01 02:00:00 0.000340 0.039470
2016-01-01 02:30:00 0.000353 0.039535
2016-01-01 03:00:00 0.000434 0.039386
2016-01-01 03:30:00 0.000387 0.039337
2016-01-01 04:00:00 0.000363 0.039155
2016-01-01 04:30:00 0.000349 0.039653
2016-01-01 05:00:00 0.000356 0.038921
2016-01-01 05:30:00 0.000341 0.038644
2016-01-01 06:00:00 0.000299 0.038913
2016-01-01 06:30:00 0.000343 0.039547
2016-01-01 07:00:00 0.000334 0.039463
2016-01-01 07:30:00 0.000306 0.039394
2016-01-01 08:00:00 0.000361 0.040014
2016-01-01 08:30:00 0.000308 0.039691
2016-01-01 09:00:00 0.000287 0.039694
2016-01-01 09:30:00 0.000347 0.039563
2016-01-01 10:00:00 0.000348 0.039241
2016-01-01 10:30:00 0.000334 0.039175
2016-01-01 11:00:00 0.000337 0.039387
2016-01-01 11:30:00 0.000288 0.039584
2016-01-01 12:00:00 0.000296 0.039337
2016-01-01 12:30:00 0.000344 0.039386
2016-01-01 13:00:00 0.000301 0.039237
2016-01-01 13:30:00 0.000301 0.039454
2016-01-01 14:00:00 0.000509 0.038450
2016-01-01 14:30:00 0.001317 0.039967
... ... ...
2016-12-31 23:30:00 0.000367 0.003833
2016-12-31 23:31:00 0.000383 0.003850
2016-12-31 23:32:00 0.000367 0.003867
2016-12-31 23:33:00 0.000367 0.003850
2016-12-31 23:34:00 0.000383 0.003867
2016-12-31 23:35:00 0.000367 0.003867
2016-12-31 23:36:00 0.000383 0.003850
2016-12-31 23:37:00 0.000367 0.003867
2016-12-31 23:38:00 0.000367 0.003850
2016-12-31 23:39:00 0.000383 0.003867
2016-12-31 23:40:00 0.000367 0.003867
2016-12-31 23:41:00 0.000367 0.003867
2016-12-31 23:42:00 0.000383 0.003850
2016-12-31 23:43:00 0.000367 0.003867
2016-12-31 23:44:00 0.000367 0.003883
2016-12-31 23:45:00 0.000367 0.003867
2016-12-31 23:46:00 0.000383 0.003867
2016-12-31 23:47:00 0.000367 0.003867
2016-12-31 23:48:00 0.000367 0.003867
2016-12-31 23:49:00 0.000400 0.003817
2016-12-31 23:50:00 0.000450 0.003767
2016-12-31 23:51:00 0.000450 0.003767
2016-12-31 23:52:00 0.000433 0.003783
2016-12-31 23:53:00 0.000433 0.003783
2016-12-31 23:54:00 0.000450 0.003767
2016-12-31 23:55:00 0.000433 0.003800
2016-12-31 23:56:00 0.000433 0.003783
2016-12-31 23:57:00 0.000450 0.003783
2016-12-31 23:58:00 0.000433 0.003800
2016-12-31 23:59:00 0.000433 0.003767
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
Date & Time
2016-01-01 00:00:00 0.000543 0.004767 0.007030
2016-01-01 00:30:00 0.000783 0.004856 0.043428
2016-01-01 01:00:00 0.000757 0.066279 0.134296
2016-01-01 01:30:00 0.000589 0.004738 0.091560
2016-01-01 02:00:00 0.000666 0.004804 0.004786
2016-01-01 02:30:00 0.000738 0.004861 0.092701
2016-01-01 03:00:00 0.000295 0.004696 0.209441
2016-01-01 03:30:00 0.000607 0.004814 0.143477
2016-01-01 04:00:00 0.000627 0.004823 0.117937
2016-01-01 04:30:00 0.000071 0.004592 0.004542
2016-01-01 05:00:00 0.000574 0.004769 0.101941
2016-01-01 05:30:00 0.000769 0.004864 0.101054
2016-01-01 06:00:00 0.000706 0.004843 0.004496
2016-01-01 06:30:00 0.000714 0.004927 0.103627
2016-01-01 07:00:00 0.000775 0.004897 0.084492
2016-01-01 07:30:00 0.000630 0.004870 0.004572
2016-01-01 08:00:00 0.000728 0.004962 0.114857
2016-01-01 08:30:00 0.001001 0.005088 0.065381
2016-01-01 09:00:00 0.001002 0.005067 0.004549
2016-01-01 09:30:00 0.001049 0.005122 0.117674
2016-01-01 10:00:00 0.001124 0.005151 0.046622
2016-01-01 10:30:00 0.001090 0.005120 0.046247
2016-01-01 11:00:00 0.001072 0.005053 0.119261
2016-01-01 11:30:00 0.001000 0.004980 0.004462
2016-01-01 12:00:00 0.001001 0.004949 0.023140
2016-01-01 12:30:00 0.001001 0.005013 0.125196
2016-01-01 13:00:00 0.000999 0.004976 0.004481
2016-01-01 13:30:00 0.000984 0.004975 0.012528
2016-01-01 14:00:00 0.000749 0.004768 0.131491
2016-01-01 14:30:00 0.000098 0.004186 0.006492
... ... ... ...
2016-12-31 23:30:00 0.046783 0.004850 0.003083
2016-12-31 23:31:00 0.046000 0.004833 0.003083
2016-12-31 23:32:00 0.046033 0.004817 0.003083
2016-12-31 23:33:00 0.046000 0.004833 0.003100
2016-12-31 23:34:00 0.045983 0.004833 0.003083
2016-12-31 23:35:00 0.045983 0.004817 0.003100
2016-12-31 23:36:00 0.046000 0.004817 0.003083
2016-12-31 23:37:00 0.045917 0.004833 0.003100
2016-12-31 23:38:00 0.046000 0.004833 0.003083
2016-12-31 23:39:00 0.045933 0.004817 0.003100
2016-12-31 23:40:00 0.045900 0.004833 0.003083
2016-12-31 23:41:00 0.045617 0.004817 0.003083
2016-12-31 23:42:00 0.045617 0.004833 0.003083
2016-12-31 23:43:00 0.045633 0.004833 0.003100
2016-12-31 23:44:00 0.045667 0.004833 0.003100
2016-12-31 23:45:00 0.045983 0.004833 0.003100
2016-12-31 23:46:00 0.045733 0.004850 0.003083
2016-12-31 23:47:00 0.045700 0.004850 0.003100
2016-12-31 23:48:00 0.045850 0.004850 0.003117
2016-12-31 23:49:00 0.045850 0.004850 0.051700
2016-12-31 23:50:00 0.046000 0.004933 0.145600
2016-12-31 23:51:00 0.045883 0.004917 0.130200
2016-12-31 23:52:00 0.045983 0.004917 0.129567
2016-12-31 23:53:00 0.045900 0.004917 0.128517
2016-12-31 23:54:00 0.045883 0.004917 0.128583
2016-12-31 23:55:00 0.045867 0.004900 0.127633
2016-12-31 23:56:00 0.045967 0.004900 0.127083
2016-12-31 23:57:00 0.045933 0.004917 0.126417
2016-12-31 23:58:00 0.046017 0.004900 0.125833
2016-12-31 23:59:00 0.046067 0.004917 0.125283
[247600 rows x 17 columns]>
print(homeb.head(10))
print(homeb.tail())
use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
Date & Time
2016-01-01 00:00:00 0.455814 0.0 0.455814 0.001166 0.189853
2016-01-01 00:30:00 0.403376 0.0 0.403376 0.000659 0.104036
2016-01-01 01:00:00 0.662962 0.0 0.662962 0.000823 0.107150
2016-01-01 01:30:00 0.677851 0.0 0.677851 0.001426 0.196811
2016-01-01 02:00:00 0.425556 0.0 0.425556 0.000759 0.119401
2016-01-01 02:30:00 0.448744 0.0 0.448744 0.000652 0.124341
2016-01-01 03:00:00 0.751886 0.0 0.751886 0.001643 0.325440
2016-01-01 03:30:00 0.554862 0.0 0.554862 0.001001 0.195691
2016-01-01 04:00:00 0.481422 0.0 0.481422 0.000929 0.183667
2016-01-01 04:30:00 0.640826 0.0 0.640826 0.002342 0.434424
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
Date & Time
2016-01-01 00:00:00 0.006457 0.003416 0.032004
2016-01-01 00:30:00 0.006415 0.003429 0.031977
2016-01-01 01:00:00 0.006254 0.003527 0.119877
2016-01-01 01:30:00 0.006257 0.003562 0.132939
2016-01-01 02:00:00 0.006401 0.003441 0.054034
2016-01-01 02:30:00 0.006592 0.003307 0.003767
2016-01-01 03:00:00 0.006718 0.003231 0.003629
2016-01-01 03:30:00 0.006653 0.003261 0.003701
2016-01-01 04:00:00 0.006651 0.003241 0.003699
2016-01-01 04:30:00 0.021909 0.003182 0.003640
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
Date & Time
2016-01-01 00:00:00 0.002309 0.004976
2016-01-01 00:30:00 0.002378 0.004958
2016-01-01 01:00:00 0.002413 0.012929
2016-01-01 01:30:00 0.002367 0.004988
2016-01-01 02:00:00 0.002423 0.005025
2016-01-01 02:30:00 0.002330 0.004991
2016-01-01 03:00:00 0.002167 0.005024
2016-01-01 03:30:00 0.002261 0.005003
2016-01-01 04:00:00 0.002260 0.004975
2016-01-01 04:30:00 0.002108 0.005061
MBed + KBed outlets [kW] Dryer + egauge [kW] \
Date & Time
2016-01-01 00:00:00 0.090664 0.000021
2016-01-01 00:30:00 0.090591 0.000068
2016-01-01 01:00:00 0.090467 0.000173
2016-01-01 01:30:00 0.090688 0.000123
2016-01-01 02:00:00 0.077748 0.000040
2016-01-01 02:30:00 0.068908 0.000068
2016-01-01 03:00:00 0.068744 0.000064
2016-01-01 03:30:00 0.063393 0.000081
2016-01-01 04:00:00 0.056010 0.000059
2016-01-01 04:30:00 0.055681 0.000080
Panel GFI (central vac) [kW] Home Office (R) [kW] \
Date & Time
2016-01-01 00:00:00 0.000347 0.039824
2016-01-01 00:30:00 0.000353 0.039098
2016-01-01 01:00:00 0.000429 0.038571
2016-01-01 01:30:00 0.000429 0.039092
2016-01-01 02:00:00 0.000340 0.039470
2016-01-01 02:30:00 0.000353 0.039535
2016-01-01 03:00:00 0.000434 0.039386
2016-01-01 03:30:00 0.000387 0.039337
2016-01-01 04:00:00 0.000363 0.039155
2016-01-01 04:30:00 0.000349 0.039653
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
Date & Time
2016-01-01 00:00:00 0.000543 0.004767 0.007030
2016-01-01 00:30:00 0.000783 0.004856 0.043428
2016-01-01 01:00:00 0.000757 0.066279 0.134296
2016-01-01 01:30:00 0.000589 0.004738 0.091560
2016-01-01 02:00:00 0.000666 0.004804 0.004786
2016-01-01 02:30:00 0.000738 0.004861 0.092701
2016-01-01 03:00:00 0.000295 0.004696 0.209441
2016-01-01 03:30:00 0.000607 0.004814 0.143477
2016-01-01 04:00:00 0.000627 0.004823 0.117937
2016-01-01 04:30:00 0.000071 0.004592 0.004542
use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
Date & Time
2016-12-31 23:55:00 0.406400 0.0 0.406400 0.000267 0.008950
2016-12-31 23:56:00 0.405350 0.0 0.405350 0.000267 0.008950
2016-12-31 23:57:00 0.404333 0.0 0.404333 0.000267 0.008900
2016-12-31 23:58:00 0.403883 0.0 0.403883 0.000267 0.008900
2016-12-31 23:59:00 0.382550 0.0 0.382550 0.000200 0.008883
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
Date & Time
2016-12-31 23:55:00 0.005900 0.003450 0.051567
2016-12-31 23:56:00 0.005883 0.003433 0.051533
2016-12-31 23:57:00 0.005900 0.003433 0.051517
2016-12-31 23:58:00 0.005900 0.003450 0.051517
2016-12-31 23:59:00 0.005917 0.003367 0.051400
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
Date & Time
2016-12-31 23:55:00 0.003333 0.004950
2016-12-31 23:56:00 0.003350 0.004967
2016-12-31 23:57:00 0.003350 0.004967
2016-12-31 23:58:00 0.003350 0.004967
2016-12-31 23:59:00 0.003333 0.004967
MBed + KBed outlets [kW] Dryer + egauge [kW] \
Date & Time
2016-12-31 23:55:00 0.090567 0.000200
2016-12-31 23:56:00 0.090017 0.000200
2016-12-31 23:57:00 0.089817 0.000200
2016-12-31 23:58:00 0.089850 0.000200
2016-12-31 23:59:00 0.068817 0.000167
Panel GFI (central vac) [kW] Home Office (R) [kW] \
Date & Time
2016-12-31 23:55:00 0.000433 0.003800
2016-12-31 23:56:00 0.000433 0.003783
2016-12-31 23:57:00 0.000450 0.003783
2016-12-31 23:58:00 0.000433 0.003800
2016-12-31 23:59:00 0.000433 0.003767
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
Date & Time
2016-12-31 23:55:00 0.045867 0.004900 0.127633
2016-12-31 23:56:00 0.045967 0.004900 0.127083
2016-12-31 23:57:00 0.045933 0.004917 0.126417
2016-12-31 23:58:00 0.046017 0.004900 0.125833
2016-12-31 23:59:00 0.046067 0.004917 0.125283
homeb_description=homeb.describe()
print (homeb_description)
use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
count 247600.000000 247600.0 247600.000000 247600.000000 247600.000000
mean 1.035553 0.0 1.035553 0.342994 0.116331
std 1.017468 0.0 1.017468 0.868884 0.181299
min 0.004017 0.0 0.004017 0.000000 0.000003
25% 0.381183 0.0 0.381183 0.000100 0.009000
50% 0.670183 0.0 0.670183 0.000350 0.009350
75% 1.094150 0.0 1.094150 0.002483 0.260437
max 9.381150 0.0 9.381150 4.303854 0.586683
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
count 247600.000000 247600.000000 247600.000000
mean 0.008922 0.004404 0.031873
std 0.017014 0.015072 0.039393
min 0.000017 0.000000 0.000033
25% 0.005517 0.003083 0.003800
50% 0.005817 0.003233 0.030450
75% 0.005983 0.003350 0.031983
max 0.186483 0.465350 0.878296
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
count 247600.000000 247600.000000
mean 0.160300 0.005410
std 0.250489 0.007141
min 0.000267 0.000017
25% 0.003267 0.004800
50% 0.003533 0.004900
75% 0.272192 0.004983
max 1.546676 0.697683
MBed + KBed outlets [kW] Dryer + egauge [kW] \
count 247600.000000 247600.000000
mean 0.082901 0.022582
std 0.122644 0.290628
min 0.000033 0.000000
25% 0.056850 0.000033
50% 0.062683 0.000067
75% 0.070833 0.000100
max 2.592283 6.054800
Panel GFI (central vac) [kW] Home Office (R) [kW] \
count 247600.000000 247600.000000
mean 0.000404 0.123603
std 0.007269 0.167647
min 0.000000 0.000000
25% 0.000217 0.003067
50% 0.000300 0.016644
75% 0.000367 0.339850
max 0.768017 0.500833
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
count 247600.000000 247600.000000 247600.000000
mean 0.040586 0.012301 0.072069
std 0.019152 0.102988 0.076897
min 0.000000 0.000067 0.000867
25% 0.032767 0.004433 0.004000
50% 0.033750 0.004683 0.045383
75% 0.045183 0.004883 0.133167
max 0.911288 1.923833 1.132350
print(len(homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59","AC [kW]"]))
print(homeb.loc["2016-01-02 00:00:00":"2016-01-02 23:59:59","AC [kW]"].shape)
print(homeb.loc["2016-01-03 00:00:00":"2016-01-03 23:59:59","AC [kW]"].shape)
48 (48,) (48,)
ac_data=homeb.loc[:,"AC [kW]"]
washer_data=homeb.loc[:,"Washer [kW]"]
grid_data=homeb.loc[:,"Grid [kW]"]
ac_data_daywise=homeb.loc["2016-12-06 00:00:00":"2016-12-06 23:59:59","AC [kW]"] ac_data_daywise.describe() ac_data_daywise=homeb.loc["2016-12-06 00:00:00":"2016-12-06 23:59:59","AC [kW]"] ac_data_daywise.describe()
ac_data_daywise=homeb.loc["2016-09-03 00:00:00":"2016-09-03 23:59:59","AC [kW]"] ac_data_daywise.describe()
import datetime
start_date=datetime.date(2016,1,1)
end_date=datetime.date(2016,12,31)
inc_1_days=datetime.timedelta(days=1)
#print (start_date)
#print (start_date+inc_1_days)
ac_data_list=[]
while (start_date <= end_date):
#print (start_date)
#print(len(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"]))
#print(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"].max())
ac_data_list.append({"date":str(start_date),"no_of_data":len(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"]),"Peak_Power":homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"].max()})
start_date=start_date+inc_1_days
#print (ac_data)
ac_data_day_wise=pd.DataFrame(ac_data_list)
ac_data_day_wise.describe(include='O')
ac_data_day_wise.describe()
ac_data_day_wise_1440=ac_data_day_wise[ac_data_day_wise["no_of_data"]==1440]
ac_data_day_wise.set_index("date",inplace=True)
ac_data_day_wise_1440.set_index("date",inplace=True)
pd.set_option('display.max_rows', 1000)
print(ac_data_day_wise["no_of_data"].value_counts())
print(ac_data_day_wise_1440.info)
48 199 1440 164 1500 1 342 1 46 1 Name: no_of_data, dtype: int64 <bound method DataFrame.info of Peak_Power no_of_data date 2016-07-20 2.541317 1440 2016-07-21 2.739450 1440 2016-07-22 2.868167 1440 2016-07-23 2.805167 1440 2016-07-24 2.760617 1440 2016-07-25 2.816733 1440 2016-07-26 2.761800 1440 2016-07-27 2.808667 1440 2016-07-28 2.773983 1440 2016-07-29 2.731933 1440 2016-07-30 2.664517 1440 2016-07-31 2.473683 1440 2016-08-01 2.520417 1440 2016-08-02 2.503650 1440 2016-08-03 2.646683 1440 2016-08-04 2.660650 1440 2016-08-05 2.730233 1440 2016-08-06 2.723617 1440 2016-08-07 2.690383 1440 2016-08-08 2.676767 1440 2016-08-09 2.722517 1440 2016-08-10 2.634617 1440 2016-08-11 2.809017 1440 2016-08-12 2.847450 1440 2016-08-13 2.828167 1440 2016-08-14 2.822833 1440 2016-08-15 2.723917 1440 2016-08-16 2.676733 1440 2016-08-17 2.624067 1440 2016-08-18 2.742367 1440 2016-08-19 2.702283 1440 2016-08-20 2.695400 1440 2016-08-21 2.652767 1440 2016-08-22 2.496350 1440 2016-08-23 2.635567 1440 2016-08-24 2.692117 1440 2016-08-25 2.697917 1440 2016-08-26 2.771733 1440 2016-08-27 2.731900 1440 2016-08-28 2.725483 1440 2016-08-29 2.706500 1440 2016-08-30 2.707133 1440 2016-08-31 2.603433 1440 2016-09-01 2.559233 1440 2016-09-02 2.572700 1440 2016-09-03 0.003183 1440 2016-09-04 0.003433 1440 2016-09-05 0.003267 1440 2016-09-06 2.485983 1440 2016-09-07 2.455917 1440 2016-09-08 2.762900 1440 2016-09-09 2.762267 1440 2016-09-10 2.688500 1440 2016-09-11 2.611383 1440 2016-09-12 0.003100 1440 2016-09-13 0.003383 1440 2016-09-14 0.003450 1440 2016-09-15 0.002967 1440 2016-09-16 0.001733 1440 2016-09-17 0.000750 1440 2016-09-18 2.594850 1440 2016-09-19 2.479600 1440 2016-09-20 2.616400 1440 2016-09-21 2.595167 1440 2016-09-22 2.693433 1440 2016-09-23 2.579983 1440 2016-09-24 0.002900 1440 2016-09-25 0.002517 1440 2016-09-26 0.000933 1440 2016-09-27 0.003017 1440 2016-09-28 0.003867 1440 2016-09-29 0.003467 1440 2016-09-30 0.003033 1440 2016-10-01 0.003917 1440 2016-10-02 0.002950 1440 2016-10-03 0.003417 1440 2016-10-04 0.003083 1440 2016-10-05 0.003467 1440 2016-10-06 0.003033 1440 2016-10-07 0.003200 1440 2016-10-08 0.002667 1440 2016-10-09 0.003083 1440 2016-10-10 0.003800 1440 2016-10-11 0.010300 1440 2016-10-12 0.006000 1440 2016-10-13 0.002850 1440 2016-10-14 0.003083 1440 2016-10-15 0.006183 1440 2016-10-16 0.000283 1440 2016-10-17 0.002733 1440 2016-10-18 0.002983 1440 2016-10-19 0.005633 1440 2016-10-20 0.002767 1440 2016-10-21 0.003217 1440 2016-10-22 0.002883 1440 2016-10-23 0.002550 1440 2016-10-24 0.003767 1440 2016-10-25 0.006300 1440 2016-10-26 0.002900 1440 2016-10-27 0.002950 1440 2016-10-28 0.002783 1440 2016-10-29 0.006450 1440 2016-10-30 0.006367 1440 2016-10-31 0.002817 1440 2016-11-01 0.008817 1440 2016-11-02 0.002483 1440 2016-11-03 0.002383 1440 2016-11-04 0.005617 1440 2016-11-05 0.005150 1440 2016-11-07 0.003067 1440 2016-11-08 0.003217 1440 2016-11-09 0.003517 1440 2016-11-10 0.002967 1440 2016-11-11 0.003033 1440 2016-11-12 0.003000 1440 2016-11-13 0.003000 1440 2016-11-14 0.003083 1440 2016-11-15 0.003100 1440 2016-11-16 0.002783 1440 2016-11-17 0.003117 1440 2016-11-18 0.006500 1440 2016-11-19 0.009517 1440 2016-11-20 0.008400 1440 2016-11-21 0.005567 1440 2016-11-22 0.009583 1440 2016-11-23 0.003567 1440 2016-11-24 0.003683 1440 2016-11-25 0.003017 1440 2016-11-26 0.006117 1440 2016-11-27 0.002917 1440 2016-11-28 0.003500 1440 2016-11-29 0.002983 1440 2016-11-30 0.002850 1440 2016-12-01 0.002967 1440 2016-12-02 0.003100 1440 2016-12-03 0.003000 1440 2016-12-04 0.003650 1440 2016-12-05 0.003417 1440 2016-12-06 0.004017 1440 2016-12-07 0.003050 1440 2016-12-08 0.003600 1440 2016-12-09 0.003167 1440 2016-12-10 0.003183 1440 2016-12-11 0.003567 1440 2016-12-12 0.003067 1440 2016-12-13 0.003350 1440 2016-12-14 0.003800 1440 2016-12-15 0.009500 1440 2016-12-16 0.003733 1440 2016-12-17 0.009033 1440 2016-12-18 0.003533 1440 2016-12-19 0.003533 1440 2016-12-20 0.009300 1440 2016-12-21 0.009250 1440 2016-12-22 0.010150 1440 2016-12-23 0.009300 1440 2016-12-24 0.003300 1440 2016-12-25 0.003133 1440 2016-12-26 0.003533 1440 2016-12-27 0.003600 1440 2016-12-28 0.003183 1440 2016-12-29 0.009717 1440 2016-12-30 0.003233 1440 2016-12-31 0.003283 1440>
plt.figure()
fig, axes = plt.subplots(nrows=2, ncols=2)
ac_data.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59"].plot.line(ax=axes[0,0])
ac_data.loc["2016-01-02 00:00:00":"2016-01-02 23:59:59"].plot.line(style='g', ax=axes[0,0])
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=axes[0,1])
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(style='g',ax=axes[1,1])
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(style='g',ax=axes[1,0])
<matplotlib.axes._subplots.AxesSubplot at 0x12aa3240>
<Figure size 432x288 with 0 Axes>
plt.figure(1)
fig, axes = plt.subplots(nrows=10, ncols=1)
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=axes[0])
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(style='g', ax=axes[1])
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=axes[2])
ac_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(style='g',ax=axes[3])
ac_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(style='g',ax=axes[4])
ac_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=axes[5])
ac_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(style='g', ax=axes[6])
ac_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=axes[7])
ac_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(style='g',ax=axes[8])
ac_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(style='g',ax=axes[9])
<matplotlib.axes._subplots.AxesSubplot at 0x12c50080>
<Figure size 432x288 with 0 Axes>
plt.figure(1)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-20")
ax[1].set_title("Grid Data 2016-07-20")
plt.figure(2)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-21")
ax[1].set_title("Grid Data 2016-07-21")
plt.figure(3)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-22")
ax[1].set_title("Grid Data 2016-07-22")
plt.figure(4)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-23")
ax[1].set_title("Grid Data 2016-07-23")
plt.figure(5)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-24")
ax[1].set_title("Grid Data 2016-07-24")
plt.figure(6)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-25")
ax[1].set_title("Grid Data 2016-07-25")
plt.figure(7)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-26")
ax[1].set_title("Grid Data 2016-07-26")
plt.figure(8)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-27")
ax[1].set_title("Grid Data 2016-07-27")
plt.figure(9)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-28")
ax[1].set_title("Grid Data 2016-07-28")
plt.figure(10)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data")
ax[1].set_title("Grid Data")
Text(0.5,1,'Grid Data')
<Figure size 432x288 with 0 Axes>
training_iters = 2000
learning_rate = 0.001
batch_size = 128
#both placeholders are of type float
x1 = tf.placeholder("float", [None, 1,1440,1])
y1 = tf.placeholder("int64", [None, 2])
train_X=ac_data.loc["2016-07-20 00:00:00":"2016-11-05 23:59:59"]
rows=train_X.size/1440
print("rows = "+str(rows))
train_X=train_X.append(washer_data.loc["2016-11-07 00:00:00":"2016-12-31 23:59:59"])
test_X=washer_data.loc["2016-07-20 00:00:00":"2016-11-05 23:59:59"]
test_X=test_X.append(ac_data.loc["2016-11-07 00:00:00":"2016-12-31 23:59:59"])
print ("train_X size= "+str(train_X.size))
print("train_X shape= "+str(train_X.shape))
train_X=train_X.values.reshape(164,1,1440,1)
test_X=test_X.values.reshape(164,1,1440,1)
train_Y=pd.DataFrame([])
test_Y=pd.DataFrame([])
print ("after reshape train_X[0] shape= "+ str(train_X.shape[0]))
for i in range(train_X.shape[0]):
if i<rows:
train_Y=train_Y.append(pd.DataFrame({"Sno":[i],"AC":1,"other":0}))
else:
train_Y=train_Y.append(pd.DataFrame({"Sno":[i],"AC":0,"other":1}))
for i in range(train_X.shape[0]):
if i<rows:
test_Y=test_Y.append(pd.DataFrame({"Sno":[i],"AC":0,"other":1}))
else:
test_Y=test_Y.append(pd.DataFrame({"Sno":[i],"AC":1,"other":0}))
train_Y.set_index("Sno",inplace=True)
train_Y=train_Y.values.reshape(164,2)
test_Y.set_index("Sno",inplace=True)
test_Y=test_Y.values.reshape(164,2)
print("train_Y data = "+str(train_Y))
print ("train_Y shape= "+str(train_Y.shape))
print ("test_Y shape= "+str(test_Y.shape))
print ("train_X size= "+str(train_X.size))
print("train_X shape= "+str(train_X.shape))
rows = 109.0 train_X size= 236160 train_X shape= (236160,) after reshape train_X[0] shape= 164 train_Y data = [[1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1]] train_Y shape= (164, 2) test_Y shape= (164, 2) train_X size= 236160 train_X shape= (164, 1, 1440, 1)
plt.figure()
ndays=82
f,axes=plt.subplots(nrows=ndays,ncols=2,figsize=(20,200))
for i in range(0,ndays*2,2):
axes[int(i/2),0].plot(train_X[i,0])
axes[int(i/2),1].plot(train_X[i+1,0])
if (train_Y[i,0])==1:
axes[int(i/2),0].set_title("AC Data")
else:
axes[int(i/2),0].set_title("Washer Data")
if (train_Y[i+1,0])==1:
axes[int(i/2),1].set_title("AC Data")
else:
axes[int(i/2),1].set_title("Washer Data")
plt.subplots_adjust(bottom=0.1, right=0.8, top=4)
plt.show()
<Figure size 432x288 with 0 Axes>
plt.figure()
ndays=82
f,axes=plt.subplots(nrows=ndays,ncols=2,figsize=(20,200))
for i in range(0,ndays*2,2):
axes[int(i/2),0].plot(test_X[i,0])
axes[int(i/2),1].plot(test_X[i+1,0])
if (test_Y[i,0])==1:
axes[int(i/2),0].set_title("AC Data")
else:
axes[int(i/2),0].set_title("Washer Data")
if (test_Y[i+1,0])==1:
axes[int(i/2),1].set_title("AC Data")
else:
axes[int(i/2),1].set_title("Washer Data")
plt.subplots_adjust(bottom=0.1, right=0.8, top=4)
plt.show()
<Figure size 432x288 with 0 Axes>
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')
with tf.variable_scope("",reuse = tf.AUTO_REUSE):
weights = {
'wc1': tf.get_variable('W1', shape=(1,3,1,32), initializer=tf.contrib.layers.xavier_initializer()),
'wc2': tf.get_variable('W2', shape=(1,3,32,64), initializer=tf.contrib.layers.xavier_initializer()),
'wc3': tf.get_variable('W3', shape=(1,3,64,128), initializer=tf.contrib.layers.xavier_initializer()),
'wd1': tf.get_variable('W4', shape=(180*128,128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('W5', shape=(128,2), initializer=tf.contrib.layers.xavier_initializer()),
}
biases = {
'bc1': tf.get_variable('B1', shape=(32), initializer=tf.contrib.layers.xavier_initializer()),
'bc2': tf.get_variable('B2', shape=(64), initializer=tf.contrib.layers.xavier_initializer()),
'bc3': tf.get_variable('B3', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'bd1': tf.get_variable('B4', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('B5', shape=(2), initializer=tf.contrib.layers.xavier_initializer()),
}
def conv_net(x, weights, biases):
# here we call the conv2d function we had defined above and pass the input image x, weights wc1 and bias bc1.
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 14*14 matrix.
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
# here we call the conv2d function we had defined above and pass the input image x, weights wc2 and bias bc2.
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 7*7 matrix.
conv2 = maxpool2d(conv2, k=2)
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 4*4.
conv3 = maxpool2d(conv3, k=2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Output, class prediction
# finally we multiply the fully connected layer with the weights and add a bias term.
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
pred = conv_net(x1, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y1))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
WARNING:tensorflow:From <ipython-input-21-0d1f03f2adbb>:3: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version. Instructions for updating: Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default. See tf.nn.softmax_cross_entropy_with_logits_v2.
#Here you check whether the index of the maximum value of the predicted image is equal to the actual labelled image. and both will be a column vector.
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y1, 1))
#calculate accuracy across all the given images and average them out.
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Initializing the variables
init = tf.global_variables_initializer()
print(training_iters)
2000
with tf.Session() as sess:
sess.run(init)
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter('./Output', sess.graph)
for i in range(training_iters):
for batch in range(len(train_X)//batch_size):
batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
batch_y = train_Y[batch*batch_size:min((batch+1)*batch_size,len(train_Y))]
'''print (batch_x.shape)
print (batch_x.dtype)
print (batch_y.shape)
print (batch_y.dtype)'''
batch_x.astype(float)
'''plt.figure()
plt.plot(batch_x[1,0])'''
# Run optimization op (backprop).
# Calculate batch loss and accuracy
opt = sess.run(optimizer, feed_dict={x1: batch_x,
y1: batch_y})
loss, acc = sess.run([cost, accuracy], feed_dict={x1: batch_x,
y1: batch_y})
print("Iter " + str(i) + ", Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
print("Optimization Finished!")
# Calculate accuracy for all 10000 mnist test images
test_acc,valid_loss = sess.run([accuracy,cost], feed_dict={x1: test_X, y1: test_Y})
train_loss.append(loss)
test_loss.append(valid_loss)
train_accuracy.append(acc)
test_accuracy.append(test_acc)
print("Testing Accuracy:","{:.5f}".format(test_acc))
save_path = saver.save(sess, "/tmp/model.ckpt",global_step=i,max_to_keep=500,keep_checkpoint_every_n_hours=0.1,)
//save_path = saver.save(sess, "/tmp/model"+str(i)+".ckpt",max_to_keep=500,keep_checkpoint_every_n_hours=0.5,)
print ("data saved in","/tmp/model"+str(i)+".ckpt")
summary_writer.close()
Iter 0, Loss= 1.400497, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model0.ckpt Iter 1, Loss= 1.461437, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model1.ckpt Iter 2, Loss= 1.208775, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model2.ckpt Iter 3, Loss= 0.941657, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model3.ckpt Iter 4, Loss= 0.698538, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model4.ckpt Iter 5, Loss= 0.482386, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model5.ckpt Iter 6, Loss= 0.352138, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model6.ckpt Iter 7, Loss= 0.333581, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model7.ckpt Iter 8, Loss= 0.388094, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model8.ckpt Iter 9, Loss= 0.421045, Training Accuracy= 0.59375 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model9.ckpt Iter 10, Loss= 0.405410, Training Accuracy= 0.59375 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model10.ckpt Iter 11, Loss= 0.362444, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model11.ckpt Iter 12, Loss= 0.327008, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model12.ckpt Iter 13, Loss= 0.328009, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model13.ckpt Iter 14, Loss= 0.353359, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model14.ckpt Iter 15, Loss= 0.363980, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model15.ckpt Iter 16, Loss= 0.351866, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model16.ckpt Iter 17, Loss= 0.332383, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model17.ckpt Iter 18, Loss= 0.322081, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model18.ckpt Iter 19, Loss= 0.326000, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model19.ckpt Iter 20, Loss= 0.335164, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model20.ckpt Iter 21, Loss= 0.339735, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model21.ckpt Iter 22, Loss= 0.336444, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model22.ckpt Iter 23, Loss= 0.328512, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model23.ckpt Iter 24, Loss= 0.322519, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model24.ckpt Iter 25, Loss= 0.322648, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model25.ckpt Iter 26, Loss= 0.327237, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model26.ckpt Iter 27, Loss= 0.330693, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model27.ckpt Iter 28, Loss= 0.329727, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model28.ckpt Iter 29, Loss= 0.325636, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model29.ckpt Iter 30, Loss= 0.322174, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model30.ckpt Iter 31, Loss= 0.321787, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model31.ckpt Iter 32, Loss= 0.324042, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model32.ckpt Iter 33, Loss= 0.325073, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model33.ckpt Iter 34, Loss= 0.323922, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model34.ckpt Iter 35, Loss= 0.321958, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model35.ckpt Iter 36, Loss= 0.321601, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model36.ckpt Iter 37, Loss= 0.323007, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model37.ckpt Iter 38, Loss= 0.323229, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model38.ckpt Iter 39, Loss= 0.322024, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model39.ckpt Iter 40, Loss= 0.321299, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model40.ckpt Iter 41, Loss= 0.321710, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model41.ckpt Iter 42, Loss= 0.322303, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model42.ckpt Iter 43, Loss= 0.322050, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model43.ckpt Iter 44, Loss= 0.321194, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model44.ckpt Iter 45, Loss= 0.321051, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model45.ckpt Iter 46, Loss= 0.321480, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model46.ckpt Iter 47, Loss= 0.321118, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model47.ckpt Iter 48, Loss= 0.320579, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model48.ckpt Iter 49, Loss= 0.320925, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model49.ckpt Iter 50, Loss= 0.320724, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model50.ckpt Iter 51, Loss= 0.320502, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model51.ckpt Iter 52, Loss= 0.320246, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model52.ckpt Iter 53, Loss= 0.319962, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model53.ckpt Iter 54, Loss= 0.319671, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model54.ckpt Iter 55, Loss= 0.319412, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model55.ckpt Iter 56, Loss= 0.319568, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model56.ckpt Iter 57, Loss= 0.318910, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model57.ckpt Iter 58, Loss= 0.318595, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model58.ckpt Iter 59, Loss= 0.318482, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model59.ckpt Iter 60, Loss= 0.317816, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model60.ckpt Iter 61, Loss= 0.317835, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model61.ckpt Iter 62, Loss= 0.317138, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model62.ckpt Iter 63, Loss= 0.317009, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model63.ckpt Iter 64, Loss= 0.316519, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model64.ckpt Iter 65, Loss= 0.316001, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model65.ckpt Iter 66, Loss= 0.315809, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model66.ckpt Iter 67, Loss= 0.315066, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model67.ckpt Iter 68, Loss= 0.314644, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model68.ckpt Iter 69, Loss= 0.314040, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model69.ckpt Iter 70, Loss= 0.313515, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model70.ckpt Iter 71, Loss= 0.312768, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model71.ckpt Iter 72, Loss= 0.311917, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model72.ckpt Iter 73, Loss= 0.311080, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model73.ckpt Iter 74, Loss= 0.310284, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model74.ckpt Iter 75, Loss= 0.309469, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model75.ckpt Iter 76, Loss= 0.309138, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model76.ckpt Iter 77, Loss= 0.313020, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model77.ckpt Iter 78, Loss= 0.320088, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34756 data saved in /tmp/model78.ckpt Iter 79, Loss= 0.313529, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model79.ckpt Iter 80, Loss= 0.309822, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model80.ckpt Iter 81, Loss= 0.316256, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model81.ckpt Iter 82, Loss= 0.304300, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model82.ckpt Iter 83, Loss= 0.311966, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model83.ckpt Iter 84, Loss= 0.303300, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34146 data saved in /tmp/model84.ckpt Iter 85, Loss= 0.309828, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model85.ckpt Iter 86, Loss= 0.303584, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model86.ckpt Iter 87, Loss= 0.306096, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model87.ckpt Iter 88, Loss= 0.302265, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34146 data saved in /tmp/model88.ckpt Iter 89, Loss= 0.302603, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model89.ckpt Iter 90, Loss= 0.299949, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34146 data saved in /tmp/model90.ckpt Iter 91, Loss= 0.298681, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34146 data saved in /tmp/model91.ckpt Iter 92, Loss= 0.299820, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model92.ckpt Iter 93, Loss= 0.296098, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34146 data saved in /tmp/model93.ckpt Iter 94, Loss= 0.296184, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.34146 data saved in /tmp/model94.ckpt Iter 95, Loss= 0.295835, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model95.ckpt Iter 96, Loss= 0.292744, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model96.ckpt Iter 97, Loss= 0.292456, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model97.ckpt Iter 98, Loss= 0.292152, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model98.ckpt Iter 99, Loss= 0.289842, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model99.ckpt Iter 100, Loss= 0.287651, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model100.ckpt Iter 101, Loss= 0.287374, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model101.ckpt Iter 102, Loss= 0.288161, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model102.ckpt Iter 103, Loss= 0.287896, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39024 data saved in /tmp/model103.ckpt Iter 104, Loss= 0.297124, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model104.ckpt Iter 105, Loss= 0.290272, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39024 data saved in /tmp/model105.ckpt Iter 106, Loss= 0.282895, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model106.ckpt Iter 107, Loss= 0.278054, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.36585 data saved in /tmp/model107.ckpt Iter 108, Loss= 0.281646, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39024 data saved in /tmp/model108.ckpt Iter 109, Loss= 0.292711, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35366 data saved in /tmp/model109.ckpt Iter 110, Loss= 0.283844, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model110.ckpt Iter 111, Loss= 0.275611, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model111.ckpt Iter 112, Loss= 0.270925, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.38415 data saved in /tmp/model112.ckpt Iter 113, Loss= 0.274601, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model113.ckpt Iter 114, Loss= 0.285026, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model114.ckpt Iter 115, Loss= 0.291005, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model115.ckpt Iter 116, Loss= 0.294092, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model116.ckpt Iter 117, Loss= 0.268952, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model117.ckpt Iter 118, Loss= 0.264752, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model118.ckpt Iter 119, Loss= 0.279243, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model119.ckpt Iter 120, Loss= 0.263223, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model120.ckpt Iter 121, Loss= 0.258197, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model121.ckpt Iter 122, Loss= 0.267239, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.38415 data saved in /tmp/model122.ckpt Iter 123, Loss= 0.274499, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model123.ckpt Iter 124, Loss= 0.272902, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.38415 data saved in /tmp/model124.ckpt Iter 125, Loss= 0.250898, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model125.ckpt Iter 126, Loss= 0.273416, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.44512 data saved in /tmp/model126.ckpt Iter 127, Loss= 0.314002, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model127.ckpt Iter 128, Loss= 0.247083, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model128.ckpt Iter 129, Loss= 0.352731, Training Accuracy= 0.69531 Optimization Finished! Testing Accuracy: 0.67683 data saved in /tmp/model129.ckpt Iter 130, Loss= 0.362071, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model130.ckpt Iter 131, Loss= 0.364971, Training Accuracy= 0.85938 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model131.ckpt Iter 132, Loss= 0.266649, Training Accuracy= 0.99219 Optimization Finished! Testing Accuracy: 0.53659 data saved in /tmp/model132.ckpt Iter 133, Loss= 0.323608, Training Accuracy= 0.92188 Optimization Finished! Testing Accuracy: 0.85976 data saved in /tmp/model133.ckpt Iter 134, Loss= 0.305115, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model134.ckpt Iter 135, Loss= 0.343722, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model135.ckpt Iter 136, Loss= 0.241287, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42073 data saved in /tmp/model136.ckpt Iter 137, Loss= 0.366382, Training Accuracy= 0.60938 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model137.ckpt Iter 138, Loss= 0.242643, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model138.ckpt Iter 139, Loss= 0.314098, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.35976 data saved in /tmp/model139.ckpt Iter 140, Loss= 0.272750, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.38415 data saved in /tmp/model140.ckpt Iter 141, Loss= 0.260149, Training Accuracy= 0.89844 Optimization Finished! Testing Accuracy: 0.45122 data saved in /tmp/model141.ckpt Iter 142, Loss= 0.296428, Training Accuracy= 0.99219 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model142.ckpt Iter 143, Loss= 0.254815, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39024 data saved in /tmp/model143.ckpt Iter 144, Loss= 0.296062, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.37195 data saved in /tmp/model144.ckpt Iter 145, Loss= 0.241826, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.40244 data saved in /tmp/model145.ckpt Iter 146, Loss= 0.279746, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model146.ckpt Iter 147, Loss= 0.245398, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model147.ckpt Iter 148, Loss= 0.253910, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39024 data saved in /tmp/model148.ckpt Iter 149, Loss= 0.265261, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.38415 data saved in /tmp/model149.ckpt Iter 150, Loss= 0.233340, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42073 data saved in /tmp/model150.ckpt Iter 151, Loss= 0.262895, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model151.ckpt Iter 152, Loss= 0.234434, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model152.ckpt Iter 153, Loss= 0.245627, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39634 data saved in /tmp/model153.ckpt Iter 154, Loss= 0.245920, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.39634 data saved in /tmp/model154.ckpt Iter 155, Loss= 0.229082, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model155.ckpt Iter 156, Loss= 0.246732, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.72561 data saved in /tmp/model156.ckpt Iter 157, Loss= 0.225946, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model157.ckpt Iter 158, Loss= 0.237581, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.40244 data saved in /tmp/model158.ckpt Iter 159, Loss= 0.228691, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.41463 data saved in /tmp/model159.ckpt Iter 160, Loss= 0.226892, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model160.ckpt Iter 161, Loss= 0.227794, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model161.ckpt Iter 162, Loss= 0.220847, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model162.ckpt Iter 163, Loss= 0.225418, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42073 data saved in /tmp/model163.ckpt Iter 164, Loss= 0.214629, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model164.ckpt Iter 165, Loss= 0.221716, Training Accuracy= 0.96094 Optimization Finished! Testing Accuracy: 0.46341 data saved in /tmp/model165.ckpt Iter 166, Loss= 0.210696, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model166.ckpt Iter 167, Loss= 0.215558, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model167.ckpt Iter 168, Loss= 0.206311, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model168.ckpt Iter 169, Loss= 0.210900, Training Accuracy= 0.99219 Optimization Finished! Testing Accuracy: 0.50610 data saved in /tmp/model169.ckpt Iter 170, Loss= 0.201794, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model170.ckpt Iter 171, Loss= 0.204278, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model171.ckpt Iter 172, Loss= 0.196906, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model172.ckpt Iter 173, Loss= 0.198069, Training Accuracy= 0.99219 Optimization Finished! Testing Accuracy: 0.50610 data saved in /tmp/model173.ckpt Iter 174, Loss= 0.193623, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model174.ckpt Iter 175, Loss= 0.190159, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model175.ckpt Iter 176, Loss= 0.189939, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.71951 data saved in /tmp/model176.ckpt Iter 177, Loss= 0.182804, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model177.ckpt Iter 178, Loss= 0.182128, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model178.ckpt Iter 179, Loss= 0.178770, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.71951 data saved in /tmp/model179.ckpt Iter 180, Loss= 0.173284, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.44512 data saved in /tmp/model180.ckpt Iter 181, Loss= 0.171674, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model181.ckpt Iter 182, Loss= 0.168695, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.89024 data saved in /tmp/model182.ckpt Iter 183, Loss= 0.163781, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.44512 data saved in /tmp/model183.ckpt Iter 184, Loss= 0.159804, Training Accuracy= 0.95312 Optimization Finished! Testing Accuracy: 0.46341 data saved in /tmp/model184.ckpt Iter 185, Loss= 0.157581, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.95122 data saved in /tmp/model185.ckpt Iter 186, Loss= 0.156164, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model186.ckpt Iter 187, Loss= 0.157457, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model187.ckpt Iter 188, Loss= 0.164228, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model188.ckpt Iter 189, Loss= 0.156779, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model189.ckpt Iter 190, Loss= 0.159293, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model190.ckpt Iter 191, Loss= 0.148178, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model191.ckpt Iter 192, Loss= 0.148023, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model192.ckpt Iter 193, Loss= 0.141520, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model193.ckpt Iter 194, Loss= 0.148984, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model194.ckpt Iter 195, Loss= 0.147051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model195.ckpt Iter 196, Loss= 0.179212, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model196.ckpt Iter 197, Loss= 0.151029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model197.ckpt Iter 198, Loss= 0.165130, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model198.ckpt Iter 199, Loss= 0.126043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model199.ckpt Iter 200, Loss= 0.110085, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.72561 data saved in /tmp/model200.ckpt Iter 201, Loss= 0.102300, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model201.ckpt Iter 202, Loss= 0.103080, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model202.ckpt Iter 203, Loss= 0.112821, Training Accuracy= 0.96094 Optimization Finished! Testing Accuracy: 0.46951 data saved in /tmp/model203.ckpt Iter 204, Loss= 0.122887, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98780 data saved in /tmp/model204.ckpt Iter 205, Loss= 0.180463, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model205.ckpt Iter 206, Loss= 0.128439, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model206.ckpt Iter 207, Loss= 0.123381, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model207.ckpt Iter 208, Loss= 0.090287, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model208.ckpt Iter 209, Loss= 0.081565, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model209.ckpt Iter 210, Loss= 0.092522, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.71951 data saved in /tmp/model210.ckpt Iter 211, Loss= 0.101094, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98780 data saved in /tmp/model211.ckpt Iter 212, Loss= 0.133986, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model212.ckpt Iter 213, Loss= 0.098211, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98780 data saved in /tmp/model213.ckpt Iter 214, Loss= 0.087174, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.71951 data saved in /tmp/model214.ckpt Iter 215, Loss= 0.068423, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model215.ckpt Iter 216, Loss= 0.070409, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model216.ckpt Iter 217, Loss= 0.084635, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.71951 data saved in /tmp/model217.ckpt Iter 218, Loss= 0.078530, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model218.ckpt Iter 219, Loss= 0.079462, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.72561 data saved in /tmp/model219.ckpt Iter 220, Loss= 0.061830, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model220.ckpt Iter 221, Loss= 0.056231, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model221.ckpt Iter 222, Loss= 0.062949, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model222.ckpt Iter 223, Loss= 0.062382, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model223.ckpt Iter 224, Loss= 0.062906, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model224.ckpt Iter 225, Loss= 0.051949, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model225.ckpt Iter 226, Loss= 0.047090, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model226.ckpt Iter 227, Loss= 0.050127, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model227.ckpt Iter 228, Loss= 0.049775, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model228.ckpt Iter 229, Loss= 0.050570, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model229.ckpt Iter 230, Loss= 0.043235, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model230.ckpt Iter 231, Loss= 0.039715, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model231.ckpt Iter 232, Loss= 0.040969, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model232.ckpt Iter 233, Loss= 0.041030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model233.ckpt Iter 234, Loss= 0.051607, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98780 data saved in /tmp/model234.ckpt Iter 235, Loss= 0.051533, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model235.ckpt Iter 236, Loss= 0.046531, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model236.ckpt Iter 237, Loss= 0.033847, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model237.ckpt Iter 238, Loss= 0.031146, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model238.ckpt Iter 239, Loss= 0.032710, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model239.ckpt Iter 240, Loss= 0.032334, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model240.ckpt Iter 241, Loss= 0.036018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model241.ckpt Iter 242, Loss= 0.031775, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model242.ckpt Iter 243, Loss= 0.029514, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model243.ckpt Iter 244, Loss= 0.036518, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model244.ckpt Iter 245, Loss= 0.090175, Training Accuracy= 0.95312 Optimization Finished! Testing Accuracy: 0.46341 data saved in /tmp/model245.ckpt Iter 246, Loss= 0.199504, Training Accuracy= 0.90625 Optimization Finished! Testing Accuracy: 0.85976 data saved in /tmp/model246.ckpt Iter 247, Loss= 0.571392, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model247.ckpt Iter 248, Loss= 0.066949, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.67683 data saved in /tmp/model248.ckpt Iter 249, Loss= 0.962139, Training Accuracy= 0.59375 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model249.ckpt Iter 250, Loss= 0.023450, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model250.ckpt Iter 251, Loss= 0.535348, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model251.ckpt Iter 252, Loss= 0.041904, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model252.ckpt Iter 253, Loss= 0.622273, Training Accuracy= 0.64844 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model253.ckpt Iter 254, Loss= 0.281856, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model254.ckpt Iter 255, Loss= 0.407321, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model255.ckpt Iter 256, Loss= 0.049602, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98780 data saved in /tmp/model256.ckpt Iter 257, Loss= 0.526256, Training Accuracy= 0.68750 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model257.ckpt Iter 258, Loss= 0.149295, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.43293 data saved in /tmp/model258.ckpt Iter 259, Loss= 0.470089, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model259.ckpt Iter 260, Loss= 0.227603, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model260.ckpt Iter 261, Loss= 0.141015, Training Accuracy= 0.98438 Optimization Finished! Testing Accuracy: 0.95122 data saved in /tmp/model261.ckpt Iter 262, Loss= 0.330794, Training Accuracy= 0.80469 Optimization Finished! Testing Accuracy: 0.73171 data saved in /tmp/model262.ckpt Iter 263, Loss= 0.080636, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model263.ckpt Iter 264, Loss= 0.251328, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model264.ckpt Iter 265, Loss= 0.239952, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model265.ckpt Iter 266, Loss= 0.086746, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.91463 data saved in /tmp/model266.ckpt Iter 267, Loss= 0.180952, Training Accuracy= 0.95312 Optimization Finished! Testing Accuracy: 0.90244 data saved in /tmp/model267.ckpt Iter 268, Loss= 0.163668, Training Accuracy= 0.97656 Optimization Finished! Testing Accuracy: 0.92683 data saved in /tmp/model268.ckpt Iter 269, Loss= 0.082378, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model269.ckpt Iter 270, Loss= 0.169917, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model270.ckpt Iter 271, Loss= 0.151597, Training Accuracy= 0.86719 Optimization Finished! Testing Accuracy: 0.42683 data saved in /tmp/model271.ckpt Iter 272, Loss= 0.074890, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model272.ckpt Iter 273, Loss= 0.132109, Training Accuracy= 0.99219 Optimization Finished! Testing Accuracy: 0.96341 data saved in /tmp/model273.ckpt Iter 274, Loss= 0.120372, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.97561 data saved in /tmp/model274.ckpt Iter 275, Loss= 0.072249, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model275.ckpt Iter 276, Loss= 0.115638, Training Accuracy= 0.87500 Optimization Finished! Testing Accuracy: 0.43902 data saved in /tmp/model276.ckpt Iter 277, Loss= 0.109600, Training Accuracy= 0.88281 Optimization Finished! Testing Accuracy: 0.44512 data saved in /tmp/model277.ckpt Iter 278, Loss= 0.068230, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model278.ckpt Iter 279, Loss= 0.096541, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98780 data saved in /tmp/model279.ckpt Iter 280, Loss= 0.096940, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.98171 data saved in /tmp/model280.ckpt Iter 281, Loss= 0.064198, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model281.ckpt Iter 282, Loss= 0.084722, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.70732 data saved in /tmp/model282.ckpt Iter 283, Loss= 0.085530, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.69512 data saved in /tmp/model283.ckpt Iter 284, Loss= 0.061272, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model284.ckpt Iter 285, Loss= 0.072833, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model285.ckpt Iter 286, Loss= 0.075491, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model286.ckpt Iter 287, Loss= 0.056737, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model287.ckpt Iter 288, Loss= 0.064330, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model288.ckpt Iter 289, Loss= 0.064033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model289.ckpt Iter 290, Loss= 0.052898, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model290.ckpt Iter 291, Loss= 0.058030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model291.ckpt Iter 292, Loss= 0.055838, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model292.ckpt Iter 293, Loss= 0.048330, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model293.ckpt Iter 294, Loss= 0.052144, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model294.ckpt Iter 295, Loss= 0.043559, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model295.ckpt Iter 296, Loss= 0.055987, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model296.ckpt Iter 297, Loss= 0.041216, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model297.ckpt Iter 298, Loss= 0.051783, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model298.ckpt Iter 299, Loss= 0.034579, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model299.ckpt Iter 300, Loss= 0.048289, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model300.ckpt Iter 301, Loss= 0.031080, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model301.ckpt Iter 302, Loss= 0.040942, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model302.ckpt Iter 303, Loss= 0.031163, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model303.ckpt Iter 304, Loss= 0.031377, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model304.ckpt Iter 305, Loss= 0.031866, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model305.ckpt Iter 306, Loss= 0.025293, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model306.ckpt Iter 307, Loss= 0.031091, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model307.ckpt Iter 308, Loss= 0.021775, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model308.ckpt Iter 309, Loss= 0.027173, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 0.99390 data saved in /tmp/model309.ckpt Iter 310, Loss= 0.020171, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model310.ckpt Iter 311, Loss= 0.021488, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model311.ckpt Iter 312, Loss= 0.020406, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model312.ckpt Iter 313, Loss= 0.017022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model313.ckpt Iter 314, Loss= 0.019847, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model314.ckpt Iter 315, Loss= 0.014952, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model315.ckpt Iter 316, Loss= 0.017262, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model316.ckpt Iter 317, Loss= 0.013855, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model317.ckpt Iter 318, Loss= 0.015134, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model318.ckpt Iter 319, Loss= 0.013061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model319.ckpt Iter 320, Loss= 0.013320, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model320.ckpt Iter 321, Loss= 0.012934, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model321.ckpt Iter 322, Loss= 0.010850, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model322.ckpt Iter 323, Loss= 0.011445, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model323.ckpt Iter 324, Loss= 0.009699, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model324.ckpt Iter 325, Loss= 0.010189, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model325.ckpt Iter 326, Loss= 0.008769, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model326.ckpt Iter 327, Loss= 0.009415, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model327.ckpt Iter 328, Loss= 0.008076, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model328.ckpt Iter 329, Loss= 0.008418, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model329.ckpt Iter 330, Loss= 0.007566, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model330.ckpt Iter 331, Loss= 0.007607, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model331.ckpt Iter 332, Loss= 0.006963, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model332.ckpt Iter 333, Loss= 0.006894, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model333.ckpt Iter 334, Loss= 0.006589, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model334.ckpt Iter 335, Loss= 0.006107, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model335.ckpt Iter 336, Loss= 0.006060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model336.ckpt Iter 337, Loss= 0.005586, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model337.ckpt Iter 338, Loss= 0.005586, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model338.ckpt Iter 339, Loss= 0.005150, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model339.ckpt Iter 340, Loss= 0.005138, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model340.ckpt Iter 341, Loss= 0.004810, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model341.ckpt Iter 342, Loss= 0.004677, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model342.ckpt Iter 343, Loss= 0.004555, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model343.ckpt Iter 344, Loss= 0.004295, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model344.ckpt Iter 345, Loss= 0.004266, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model345.ckpt Iter 346, Loss= 0.004057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model346.ckpt Iter 347, Loss= 0.003935, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model347.ckpt Iter 348, Loss= 0.003854, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model348.ckpt Iter 349, Loss= 0.003664, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model349.ckpt Iter 350, Loss= 0.003586, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model350.ckpt Iter 351, Loss= 0.003469, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model351.ckpt Iter 352, Loss= 0.003333, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model352.ckpt Iter 353, Loss= 0.003276, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model353.ckpt Iter 354, Loss= 0.003154, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model354.ckpt Iter 355, Loss= 0.003058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model355.ckpt Iter 356, Loss= 0.002999, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model356.ckpt Iter 357, Loss= 0.002889, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model357.ckpt Iter 358, Loss= 0.002820, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model358.ckpt Iter 359, Loss= 0.002755, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model359.ckpt Iter 360, Loss= 0.002664, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model360.ckpt Iter 361, Loss= 0.002604, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model361.ckpt Iter 362, Loss= 0.002542, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model362.ckpt Iter 363, Loss= 0.002465, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model363.ckpt Iter 364, Loss= 0.002413, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model364.ckpt Iter 365, Loss= 0.002355, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model365.ckpt Iter 366, Loss= 0.002290, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model366.ckpt Iter 367, Loss= 0.002242, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model367.ckpt Iter 368, Loss= 0.002191, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model368.ckpt Iter 369, Loss= 0.002134, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model369.ckpt Iter 370, Loss= 0.002090, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model370.ckpt Iter 371, Loss= 0.002044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model371.ckpt Iter 372, Loss= 0.001994, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model372.ckpt Iter 373, Loss= 0.001953, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model373.ckpt Iter 374, Loss= 0.001913, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model374.ckpt Iter 375, Loss= 0.001869, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model375.ckpt Iter 376, Loss= 0.001831, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model376.ckpt Iter 377, Loss= 0.001795, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model377.ckpt Iter 378, Loss= 0.001756, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model378.ckpt Iter 379, Loss= 0.001721, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model379.ckpt Iter 380, Loss= 0.001689, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model380.ckpt Iter 381, Loss= 0.001654, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model381.ckpt Iter 382, Loss= 0.001622, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model382.ckpt Iter 383, Loss= 0.001592, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model383.ckpt Iter 384, Loss= 0.001562, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model384.ckpt Iter 385, Loss= 0.001532, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model385.ckpt Iter 386, Loss= 0.001505, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model386.ckpt Iter 387, Loss= 0.001478, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model387.ckpt Iter 388, Loss= 0.001450, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model388.ckpt Iter 389, Loss= 0.001425, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model389.ckpt Iter 390, Loss= 0.001401, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model390.ckpt Iter 391, Loss= 0.001376, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model391.ckpt Iter 392, Loss= 0.001352, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model392.ckpt Iter 393, Loss= 0.001330, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model393.ckpt Iter 394, Loss= 0.001307, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model394.ckpt Iter 395, Loss= 0.001285, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model395.ckpt Iter 396, Loss= 0.001265, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model396.ckpt Iter 397, Loss= 0.001244, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model397.ckpt Iter 398, Loss= 0.001224, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model398.ckpt Iter 399, Loss= 0.001205, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model399.ckpt Iter 400, Loss= 0.001186, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model400.ckpt Iter 401, Loss= 0.001167, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model401.ckpt Iter 402, Loss= 0.001149, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model402.ckpt Iter 403, Loss= 0.001132, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model403.ckpt Iter 404, Loss= 0.001115, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model404.ckpt Iter 405, Loss= 0.001098, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model405.ckpt Iter 406, Loss= 0.001082, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model406.ckpt Iter 407, Loss= 0.001066, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model407.ckpt Iter 408, Loss= 0.001051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model408.ckpt Iter 409, Loss= 0.001036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model409.ckpt Iter 410, Loss= 0.001021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model410.ckpt Iter 411, Loss= 0.001006, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model411.ckpt Iter 412, Loss= 0.000992, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model412.ckpt Iter 413, Loss= 0.000979, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model413.ckpt Iter 414, Loss= 0.000965, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model414.ckpt Iter 415, Loss= 0.000952, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model415.ckpt Iter 416, Loss= 0.000939, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model416.ckpt Iter 417, Loss= 0.000926, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model417.ckpt Iter 418, Loss= 0.000914, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model418.ckpt Iter 419, Loss= 0.000902, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model419.ckpt Iter 420, Loss= 0.000890, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model420.ckpt Iter 421, Loss= 0.000879, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model421.ckpt Iter 422, Loss= 0.000868, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model422.ckpt Iter 423, Loss= 0.000857, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model423.ckpt Iter 424, Loss= 0.000846, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model424.ckpt Iter 425, Loss= 0.000835, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model425.ckpt Iter 426, Loss= 0.000825, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model426.ckpt Iter 427, Loss= 0.000815, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model427.ckpt Iter 428, Loss= 0.000805, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model428.ckpt Iter 429, Loss= 0.000795, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model429.ckpt Iter 430, Loss= 0.000785, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model430.ckpt Iter 431, Loss= 0.000776, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model431.ckpt Iter 432, Loss= 0.000767, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model432.ckpt Iter 433, Loss= 0.000758, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model433.ckpt Iter 434, Loss= 0.000749, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model434.ckpt Iter 435, Loss= 0.000740, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model435.ckpt Iter 436, Loss= 0.000732, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model436.ckpt Iter 437, Loss= 0.000723, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model437.ckpt Iter 438, Loss= 0.000715, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model438.ckpt Iter 439, Loss= 0.000707, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model439.ckpt Iter 440, Loss= 0.000699, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model440.ckpt Iter 441, Loss= 0.000691, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model441.ckpt Iter 442, Loss= 0.000684, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model442.ckpt Iter 443, Loss= 0.000676, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model443.ckpt Iter 444, Loss= 0.000669, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model444.ckpt Iter 445, Loss= 0.000662, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model445.ckpt Iter 446, Loss= 0.000654, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model446.ckpt Iter 447, Loss= 0.000647, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model447.ckpt Iter 448, Loss= 0.000641, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model448.ckpt Iter 449, Loss= 0.000634, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model449.ckpt Iter 450, Loss= 0.000627, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model450.ckpt Iter 451, Loss= 0.000621, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model451.ckpt Iter 452, Loss= 0.000614, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model452.ckpt Iter 453, Loss= 0.000608, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model453.ckpt Iter 454, Loss= 0.000602, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model454.ckpt Iter 455, Loss= 0.000596, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model455.ckpt Iter 456, Loss= 0.000590, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model456.ckpt Iter 457, Loss= 0.000584, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model457.ckpt Iter 458, Loss= 0.000578, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model458.ckpt Iter 459, Loss= 0.000572, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model459.ckpt Iter 460, Loss= 0.000567, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model460.ckpt Iter 461, Loss= 0.000561, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model461.ckpt Iter 462, Loss= 0.000556, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model462.ckpt Iter 463, Loss= 0.000550, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model463.ckpt Iter 464, Loss= 0.000545, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model464.ckpt Iter 465, Loss= 0.000540, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model465.ckpt Iter 466, Loss= 0.000535, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model466.ckpt Iter 467, Loss= 0.000530, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model467.ckpt Iter 468, Loss= 0.000525, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model468.ckpt Iter 469, Loss= 0.000520, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model469.ckpt Iter 470, Loss= 0.000515, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model470.ckpt Iter 471, Loss= 0.000510, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model471.ckpt Iter 472, Loss= 0.000506, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model472.ckpt Iter 473, Loss= 0.000501, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model473.ckpt Iter 474, Loss= 0.000497, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model474.ckpt Iter 475, Loss= 0.000492, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model475.ckpt Iter 476, Loss= 0.000488, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model476.ckpt Iter 477, Loss= 0.000483, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model477.ckpt Iter 478, Loss= 0.000479, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model478.ckpt Iter 479, Loss= 0.000475, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model479.ckpt Iter 480, Loss= 0.000471, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model480.ckpt Iter 481, Loss= 0.000467, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model481.ckpt Iter 482, Loss= 0.000463, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model482.ckpt Iter 483, Loss= 0.000459, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model483.ckpt Iter 484, Loss= 0.000455, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model484.ckpt Iter 485, Loss= 0.000451, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model485.ckpt Iter 486, Loss= 0.000447, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model486.ckpt Iter 487, Loss= 0.000443, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model487.ckpt Iter 488, Loss= 0.000439, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model488.ckpt Iter 489, Loss= 0.000436, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model489.ckpt Iter 490, Loss= 0.000432, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model490.ckpt Iter 491, Loss= 0.000429, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model491.ckpt Iter 492, Loss= 0.000425, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model492.ckpt Iter 493, Loss= 0.000422, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model493.ckpt Iter 494, Loss= 0.000418, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model494.ckpt Iter 495, Loss= 0.000415, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model495.ckpt Iter 496, Loss= 0.000411, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model496.ckpt Iter 497, Loss= 0.000408, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model497.ckpt Iter 498, Loss= 0.000405, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model498.ckpt Iter 499, Loss= 0.000402, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model499.ckpt Iter 500, Loss= 0.000398, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model500.ckpt Iter 501, Loss= 0.000395, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model501.ckpt Iter 502, Loss= 0.000392, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model502.ckpt Iter 503, Loss= 0.000389, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model503.ckpt Iter 504, Loss= 0.000386, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model504.ckpt Iter 505, Loss= 0.000383, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model505.ckpt Iter 506, Loss= 0.000380, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model506.ckpt Iter 507, Loss= 0.000377, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model507.ckpt Iter 508, Loss= 0.000374, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model508.ckpt Iter 509, Loss= 0.000372, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model509.ckpt Iter 510, Loss= 0.000369, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model510.ckpt Iter 511, Loss= 0.000366, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model511.ckpt Iter 512, Loss= 0.000363, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model512.ckpt Iter 513, Loss= 0.000361, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model513.ckpt Iter 514, Loss= 0.000358, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model514.ckpt Iter 515, Loss= 0.000355, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model515.ckpt Iter 516, Loss= 0.000353, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model516.ckpt Iter 517, Loss= 0.000350, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model517.ckpt Iter 518, Loss= 0.000348, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model518.ckpt Iter 519, Loss= 0.000345, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model519.ckpt Iter 520, Loss= 0.000343, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model520.ckpt Iter 521, Loss= 0.000340, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model521.ckpt Iter 522, Loss= 0.000338, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model522.ckpt Iter 523, Loss= 0.000335, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model523.ckpt Iter 524, Loss= 0.000333, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model524.ckpt Iter 525, Loss= 0.000331, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model525.ckpt Iter 526, Loss= 0.000328, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model526.ckpt Iter 527, Loss= 0.000326, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model527.ckpt Iter 528, Loss= 0.000324, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model528.ckpt Iter 529, Loss= 0.000321, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model529.ckpt Iter 530, Loss= 0.000319, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model530.ckpt Iter 531, Loss= 0.000317, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model531.ckpt Iter 532, Loss= 0.000315, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model532.ckpt Iter 533, Loss= 0.000313, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model533.ckpt Iter 534, Loss= 0.000311, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model534.ckpt Iter 535, Loss= 0.000308, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model535.ckpt Iter 536, Loss= 0.000306, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model536.ckpt Iter 537, Loss= 0.000304, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model537.ckpt Iter 538, Loss= 0.000302, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model538.ckpt Iter 539, Loss= 0.000300, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model539.ckpt Iter 540, Loss= 0.000298, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model540.ckpt Iter 541, Loss= 0.000296, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model541.ckpt Iter 542, Loss= 0.000294, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model542.ckpt Iter 543, Loss= 0.000292, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model543.ckpt Iter 544, Loss= 0.000290, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model544.ckpt Iter 545, Loss= 0.000289, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model545.ckpt Iter 546, Loss= 0.000287, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model546.ckpt Iter 547, Loss= 0.000285, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model547.ckpt Iter 548, Loss= 0.000283, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model548.ckpt Iter 549, Loss= 0.000281, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model549.ckpt Iter 550, Loss= 0.000279, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model550.ckpt Iter 551, Loss= 0.000278, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model551.ckpt Iter 552, Loss= 0.000276, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model552.ckpt Iter 553, Loss= 0.000274, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model553.ckpt Iter 554, Loss= 0.000272, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model554.ckpt Iter 555, Loss= 0.000271, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model555.ckpt Iter 556, Loss= 0.000269, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model556.ckpt Iter 557, Loss= 0.000267, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model557.ckpt Iter 558, Loss= 0.000266, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model558.ckpt Iter 559, Loss= 0.000264, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model559.ckpt Iter 560, Loss= 0.000262, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model560.ckpt Iter 561, Loss= 0.000261, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model561.ckpt Iter 562, Loss= 0.000259, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model562.ckpt Iter 563, Loss= 0.000258, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model563.ckpt Iter 564, Loss= 0.000256, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model564.ckpt Iter 565, Loss= 0.000255, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model565.ckpt Iter 566, Loss= 0.000253, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model566.ckpt Iter 567, Loss= 0.000252, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model567.ckpt Iter 568, Loss= 0.000250, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model568.ckpt Iter 569, Loss= 0.000249, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model569.ckpt Iter 570, Loss= 0.000247, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model570.ckpt Iter 571, Loss= 0.000246, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model571.ckpt Iter 572, Loss= 0.000244, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model572.ckpt Iter 573, Loss= 0.000243, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model573.ckpt Iter 574, Loss= 0.000241, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model574.ckpt Iter 575, Loss= 0.000240, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model575.ckpt Iter 576, Loss= 0.000238, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model576.ckpt Iter 577, Loss= 0.000237, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model577.ckpt Iter 578, Loss= 0.000236, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model578.ckpt Iter 579, Loss= 0.000234, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model579.ckpt Iter 580, Loss= 0.000233, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model580.ckpt Iter 581, Loss= 0.000232, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model581.ckpt Iter 582, Loss= 0.000230, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model582.ckpt Iter 583, Loss= 0.000229, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model583.ckpt Iter 584, Loss= 0.000228, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model584.ckpt Iter 585, Loss= 0.000226, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model585.ckpt Iter 586, Loss= 0.000225, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model586.ckpt Iter 587, Loss= 0.000224, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model587.ckpt Iter 588, Loss= 0.000223, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model588.ckpt Iter 589, Loss= 0.000221, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model589.ckpt Iter 590, Loss= 0.000220, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model590.ckpt Iter 591, Loss= 0.000219, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model591.ckpt Iter 592, Loss= 0.000218, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model592.ckpt Iter 593, Loss= 0.000217, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model593.ckpt Iter 594, Loss= 0.000215, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model594.ckpt Iter 595, Loss= 0.000214, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model595.ckpt Iter 596, Loss= 0.000213, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model596.ckpt Iter 597, Loss= 0.000212, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model597.ckpt Iter 598, Loss= 0.000211, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model598.ckpt Iter 599, Loss= 0.000210, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model599.ckpt Iter 600, Loss= 0.000209, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model600.ckpt Iter 601, Loss= 0.000207, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model601.ckpt Iter 602, Loss= 0.000206, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model602.ckpt Iter 603, Loss= 0.000205, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model603.ckpt Iter 604, Loss= 0.000204, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model604.ckpt Iter 605, Loss= 0.000203, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model605.ckpt Iter 606, Loss= 0.000202, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model606.ckpt Iter 607, Loss= 0.000201, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model607.ckpt Iter 608, Loss= 0.000200, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model608.ckpt Iter 609, Loss= 0.000199, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model609.ckpt Iter 610, Loss= 0.000198, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model610.ckpt Iter 611, Loss= 0.000197, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model611.ckpt Iter 612, Loss= 0.000196, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model612.ckpt Iter 613, Loss= 0.000195, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model613.ckpt Iter 614, Loss= 0.000194, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model614.ckpt Iter 615, Loss= 0.000193, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model615.ckpt Iter 616, Loss= 0.000192, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model616.ckpt Iter 617, Loss= 0.000191, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model617.ckpt Iter 618, Loss= 0.000190, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model618.ckpt Iter 619, Loss= 0.000189, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model619.ckpt Iter 620, Loss= 0.000188, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model620.ckpt Iter 621, Loss= 0.000187, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model621.ckpt Iter 622, Loss= 0.000186, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model622.ckpt Iter 623, Loss= 0.000185, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model623.ckpt Iter 624, Loss= 0.000184, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model624.ckpt Iter 625, Loss= 0.000183, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model625.ckpt Iter 626, Loss= 0.000182, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model626.ckpt Iter 627, Loss= 0.000181, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model627.ckpt Iter 628, Loss= 0.000181, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model628.ckpt Iter 629, Loss= 0.000180, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model629.ckpt Iter 630, Loss= 0.000179, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model630.ckpt Iter 631, Loss= 0.000178, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model631.ckpt Iter 632, Loss= 0.000177, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model632.ckpt Iter 633, Loss= 0.000176, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model633.ckpt Iter 634, Loss= 0.000175, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model634.ckpt Iter 635, Loss= 0.000174, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model635.ckpt Iter 636, Loss= 0.000174, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model636.ckpt Iter 637, Loss= 0.000173, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model637.ckpt Iter 638, Loss= 0.000172, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model638.ckpt Iter 639, Loss= 0.000171, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model639.ckpt Iter 640, Loss= 0.000170, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model640.ckpt Iter 641, Loss= 0.000170, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model641.ckpt Iter 642, Loss= 0.000169, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model642.ckpt Iter 643, Loss= 0.000168, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model643.ckpt Iter 644, Loss= 0.000167, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model644.ckpt Iter 645, Loss= 0.000166, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model645.ckpt Iter 646, Loss= 0.000166, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model646.ckpt Iter 647, Loss= 0.000165, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model647.ckpt Iter 648, Loss= 0.000164, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model648.ckpt Iter 649, Loss= 0.000163, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model649.ckpt Iter 650, Loss= 0.000162, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model650.ckpt Iter 651, Loss= 0.000162, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model651.ckpt Iter 652, Loss= 0.000161, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model652.ckpt Iter 653, Loss= 0.000160, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model653.ckpt Iter 654, Loss= 0.000160, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model654.ckpt Iter 655, Loss= 0.000159, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model655.ckpt Iter 656, Loss= 0.000158, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model656.ckpt Iter 657, Loss= 0.000157, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model657.ckpt Iter 658, Loss= 0.000157, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model658.ckpt Iter 659, Loss= 0.000156, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model659.ckpt Iter 660, Loss= 0.000155, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model660.ckpt Iter 661, Loss= 0.000155, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model661.ckpt Iter 662, Loss= 0.000154, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model662.ckpt Iter 663, Loss= 0.000153, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model663.ckpt Iter 664, Loss= 0.000152, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model664.ckpt Iter 665, Loss= 0.000152, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model665.ckpt Iter 666, Loss= 0.000151, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model666.ckpt Iter 667, Loss= 0.000150, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model667.ckpt Iter 668, Loss= 0.000150, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model668.ckpt Iter 669, Loss= 0.000149, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model669.ckpt Iter 670, Loss= 0.000148, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model670.ckpt Iter 671, Loss= 0.000148, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model671.ckpt Iter 672, Loss= 0.000147, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model672.ckpt Iter 673, Loss= 0.000146, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model673.ckpt Iter 674, Loss= 0.000146, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model674.ckpt Iter 675, Loss= 0.000145, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model675.ckpt Iter 676, Loss= 0.000145, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model676.ckpt Iter 677, Loss= 0.000144, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model677.ckpt Iter 678, Loss= 0.000143, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model678.ckpt Iter 679, Loss= 0.000143, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model679.ckpt Iter 680, Loss= 0.000142, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model680.ckpt Iter 681, Loss= 0.000141, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model681.ckpt Iter 682, Loss= 0.000141, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model682.ckpt Iter 683, Loss= 0.000140, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model683.ckpt Iter 684, Loss= 0.000140, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model684.ckpt Iter 685, Loss= 0.000139, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model685.ckpt Iter 686, Loss= 0.000139, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model686.ckpt Iter 687, Loss= 0.000138, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model687.ckpt Iter 688, Loss= 0.000137, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model688.ckpt Iter 689, Loss= 0.000137, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model689.ckpt Iter 690, Loss= 0.000136, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model690.ckpt Iter 691, Loss= 0.000136, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model691.ckpt Iter 692, Loss= 0.000135, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model692.ckpt Iter 693, Loss= 0.000134, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model693.ckpt Iter 694, Loss= 0.000134, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model694.ckpt Iter 695, Loss= 0.000133, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model695.ckpt Iter 696, Loss= 0.000133, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model696.ckpt Iter 697, Loss= 0.000132, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model697.ckpt Iter 698, Loss= 0.000132, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model698.ckpt Iter 699, Loss= 0.000131, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model699.ckpt Iter 700, Loss= 0.000131, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model700.ckpt Iter 701, Loss= 0.000130, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model701.ckpt Iter 702, Loss= 0.000130, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model702.ckpt Iter 703, Loss= 0.000129, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model703.ckpt Iter 704, Loss= 0.000129, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model704.ckpt Iter 705, Loss= 0.000128, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model705.ckpt Iter 706, Loss= 0.000127, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model706.ckpt Iter 707, Loss= 0.000127, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model707.ckpt Iter 708, Loss= 0.000126, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model708.ckpt Iter 709, Loss= 0.000126, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model709.ckpt Iter 710, Loss= 0.000125, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model710.ckpt Iter 711, Loss= 0.000125, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model711.ckpt Iter 712, Loss= 0.000124, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model712.ckpt Iter 713, Loss= 0.000124, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model713.ckpt Iter 714, Loss= 0.000123, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model714.ckpt Iter 715, Loss= 0.000123, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model715.ckpt Iter 716, Loss= 0.000122, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model716.ckpt Iter 717, Loss= 0.000122, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model717.ckpt Iter 718, Loss= 0.000121, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model718.ckpt Iter 719, Loss= 0.000121, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model719.ckpt Iter 720, Loss= 0.000121, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model720.ckpt Iter 721, Loss= 0.000120, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model721.ckpt Iter 722, Loss= 0.000120, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model722.ckpt Iter 723, Loss= 0.000119, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model723.ckpt Iter 724, Loss= 0.000119, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model724.ckpt Iter 725, Loss= 0.000118, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model725.ckpt Iter 726, Loss= 0.000118, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model726.ckpt Iter 727, Loss= 0.000117, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model727.ckpt Iter 728, Loss= 0.000117, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model728.ckpt Iter 729, Loss= 0.000116, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model729.ckpt Iter 730, Loss= 0.000116, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model730.ckpt Iter 731, Loss= 0.000115, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model731.ckpt Iter 732, Loss= 0.000115, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model732.ckpt Iter 733, Loss= 0.000115, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model733.ckpt Iter 734, Loss= 0.000114, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model734.ckpt Iter 735, Loss= 0.000114, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model735.ckpt Iter 736, Loss= 0.000113, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model736.ckpt Iter 737, Loss= 0.000113, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model737.ckpt Iter 738, Loss= 0.000112, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model738.ckpt Iter 739, Loss= 0.000112, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model739.ckpt Iter 740, Loss= 0.000112, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model740.ckpt Iter 741, Loss= 0.000111, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model741.ckpt Iter 742, Loss= 0.000111, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model742.ckpt Iter 743, Loss= 0.000110, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model743.ckpt Iter 744, Loss= 0.000110, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model744.ckpt Iter 745, Loss= 0.000109, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model745.ckpt Iter 746, Loss= 0.000109, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model746.ckpt Iter 747, Loss= 0.000109, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model747.ckpt Iter 748, Loss= 0.000108, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model748.ckpt Iter 749, Loss= 0.000108, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model749.ckpt Iter 750, Loss= 0.000107, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model750.ckpt Iter 751, Loss= 0.000107, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model751.ckpt Iter 752, Loss= 0.000107, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model752.ckpt Iter 753, Loss= 0.000106, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model753.ckpt Iter 754, Loss= 0.000106, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model754.ckpt Iter 755, Loss= 0.000105, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model755.ckpt Iter 756, Loss= 0.000105, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model756.ckpt Iter 757, Loss= 0.000105, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model757.ckpt Iter 758, Loss= 0.000104, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model758.ckpt Iter 759, Loss= 0.000104, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model759.ckpt Iter 760, Loss= 0.000104, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model760.ckpt Iter 761, Loss= 0.000103, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model761.ckpt Iter 762, Loss= 0.000103, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model762.ckpt Iter 763, Loss= 0.000102, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model763.ckpt Iter 764, Loss= 0.000102, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model764.ckpt Iter 765, Loss= 0.000102, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model765.ckpt Iter 766, Loss= 0.000101, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model766.ckpt Iter 767, Loss= 0.000101, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model767.ckpt Iter 768, Loss= 0.000101, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model768.ckpt Iter 769, Loss= 0.000100, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model769.ckpt Iter 770, Loss= 0.000100, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model770.ckpt Iter 771, Loss= 0.000100, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model771.ckpt Iter 772, Loss= 0.000099, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model772.ckpt Iter 773, Loss= 0.000099, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model773.ckpt Iter 774, Loss= 0.000099, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model774.ckpt Iter 775, Loss= 0.000098, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model775.ckpt Iter 776, Loss= 0.000098, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model776.ckpt Iter 777, Loss= 0.000097, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model777.ckpt Iter 778, Loss= 0.000097, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model778.ckpt Iter 779, Loss= 0.000097, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model779.ckpt Iter 780, Loss= 0.000096, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model780.ckpt Iter 781, Loss= 0.000096, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model781.ckpt Iter 782, Loss= 0.000096, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model782.ckpt Iter 783, Loss= 0.000095, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model783.ckpt Iter 784, Loss= 0.000095, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model784.ckpt Iter 785, Loss= 0.000095, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model785.ckpt Iter 786, Loss= 0.000094, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model786.ckpt Iter 787, Loss= 0.000094, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model787.ckpt Iter 788, Loss= 0.000094, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model788.ckpt Iter 789, Loss= 0.000094, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model789.ckpt Iter 790, Loss= 0.000093, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model790.ckpt Iter 791, Loss= 0.000093, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model791.ckpt Iter 792, Loss= 0.000093, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model792.ckpt Iter 793, Loss= 0.000092, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model793.ckpt Iter 794, Loss= 0.000092, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model794.ckpt Iter 795, Loss= 0.000092, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model795.ckpt Iter 796, Loss= 0.000091, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model796.ckpt Iter 797, Loss= 0.000091, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model797.ckpt Iter 798, Loss= 0.000091, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model798.ckpt Iter 799, Loss= 0.000090, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model799.ckpt Iter 800, Loss= 0.000090, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model800.ckpt Iter 801, Loss= 0.000090, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model801.ckpt Iter 802, Loss= 0.000090, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model802.ckpt Iter 803, Loss= 0.000089, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model803.ckpt Iter 804, Loss= 0.000089, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model804.ckpt Iter 805, Loss= 0.000089, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model805.ckpt Iter 806, Loss= 0.000088, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model806.ckpt Iter 807, Loss= 0.000088, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model807.ckpt Iter 808, Loss= 0.000088, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model808.ckpt Iter 809, Loss= 0.000087, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model809.ckpt Iter 810, Loss= 0.000087, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model810.ckpt Iter 811, Loss= 0.000087, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model811.ckpt Iter 812, Loss= 0.000087, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model812.ckpt Iter 813, Loss= 0.000086, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model813.ckpt Iter 814, Loss= 0.000086, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model814.ckpt Iter 815, Loss= 0.000086, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model815.ckpt Iter 816, Loss= 0.000085, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model816.ckpt Iter 817, Loss= 0.000085, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model817.ckpt Iter 818, Loss= 0.000085, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model818.ckpt Iter 819, Loss= 0.000085, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model819.ckpt Iter 820, Loss= 0.000084, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model820.ckpt Iter 821, Loss= 0.000084, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model821.ckpt Iter 822, Loss= 0.000084, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model822.ckpt Iter 823, Loss= 0.000084, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model823.ckpt Iter 824, Loss= 0.000083, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model824.ckpt Iter 825, Loss= 0.000083, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model825.ckpt Iter 826, Loss= 0.000083, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model826.ckpt Iter 827, Loss= 0.000082, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model827.ckpt Iter 828, Loss= 0.000082, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model828.ckpt Iter 829, Loss= 0.000082, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model829.ckpt Iter 830, Loss= 0.000082, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model830.ckpt Iter 831, Loss= 0.000081, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model831.ckpt Iter 832, Loss= 0.000081, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model832.ckpt Iter 833, Loss= 0.000081, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model833.ckpt Iter 834, Loss= 0.000081, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model834.ckpt Iter 835, Loss= 0.000080, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model835.ckpt Iter 836, Loss= 0.000080, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model836.ckpt Iter 837, Loss= 0.000080, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model837.ckpt Iter 838, Loss= 0.000080, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model838.ckpt Iter 839, Loss= 0.000079, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model839.ckpt Iter 840, Loss= 0.000079, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model840.ckpt Iter 841, Loss= 0.000079, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model841.ckpt Iter 842, Loss= 0.000079, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model842.ckpt Iter 843, Loss= 0.000078, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model843.ckpt Iter 844, Loss= 0.000078, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model844.ckpt Iter 845, Loss= 0.000078, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model845.ckpt Iter 846, Loss= 0.000078, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model846.ckpt Iter 847, Loss= 0.000077, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model847.ckpt Iter 848, Loss= 0.000077, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model848.ckpt Iter 849, Loss= 0.000077, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model849.ckpt Iter 850, Loss= 0.000077, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model850.ckpt Iter 851, Loss= 0.000076, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model851.ckpt Iter 852, Loss= 0.000076, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model852.ckpt Iter 853, Loss= 0.000076, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model853.ckpt Iter 854, Loss= 0.000076, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model854.ckpt Iter 855, Loss= 0.000076, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model855.ckpt Iter 856, Loss= 0.000075, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model856.ckpt Iter 857, Loss= 0.000075, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model857.ckpt Iter 858, Loss= 0.000075, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model858.ckpt Iter 859, Loss= 0.000075, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model859.ckpt Iter 860, Loss= 0.000074, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model860.ckpt Iter 861, Loss= 0.000074, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model861.ckpt Iter 862, Loss= 0.000074, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model862.ckpt Iter 863, Loss= 0.000074, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model863.ckpt Iter 864, Loss= 0.000074, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model864.ckpt Iter 865, Loss= 0.000073, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model865.ckpt Iter 866, Loss= 0.000073, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model866.ckpt Iter 867, Loss= 0.000073, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model867.ckpt Iter 868, Loss= 0.000073, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model868.ckpt Iter 869, Loss= 0.000072, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model869.ckpt Iter 870, Loss= 0.000072, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model870.ckpt Iter 871, Loss= 0.000072, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model871.ckpt Iter 872, Loss= 0.000072, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model872.ckpt Iter 873, Loss= 0.000072, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model873.ckpt Iter 874, Loss= 0.000071, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model874.ckpt Iter 875, Loss= 0.000071, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model875.ckpt Iter 876, Loss= 0.000071, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model876.ckpt Iter 877, Loss= 0.000071, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model877.ckpt Iter 878, Loss= 0.000071, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model878.ckpt Iter 879, Loss= 0.000070, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model879.ckpt Iter 880, Loss= 0.000070, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model880.ckpt Iter 881, Loss= 0.000070, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model881.ckpt Iter 882, Loss= 0.000070, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model882.ckpt Iter 883, Loss= 0.000069, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model883.ckpt Iter 884, Loss= 0.000069, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model884.ckpt Iter 885, Loss= 0.000069, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model885.ckpt Iter 886, Loss= 0.000069, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model886.ckpt Iter 887, Loss= 0.000069, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model887.ckpt Iter 888, Loss= 0.000068, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model888.ckpt Iter 889, Loss= 0.000068, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model889.ckpt Iter 890, Loss= 0.000068, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model890.ckpt Iter 891, Loss= 0.000068, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model891.ckpt Iter 892, Loss= 0.000068, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model892.ckpt Iter 893, Loss= 0.000068, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model893.ckpt Iter 894, Loss= 0.000067, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model894.ckpt Iter 895, Loss= 0.000067, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model895.ckpt Iter 896, Loss= 0.000067, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model896.ckpt Iter 897, Loss= 0.000067, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model897.ckpt Iter 898, Loss= 0.000067, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model898.ckpt Iter 899, Loss= 0.000066, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model899.ckpt Iter 900, Loss= 0.000066, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model900.ckpt Iter 901, Loss= 0.000066, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model901.ckpt Iter 902, Loss= 0.000066, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model902.ckpt Iter 903, Loss= 0.000066, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model903.ckpt Iter 904, Loss= 0.000065, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model904.ckpt Iter 905, Loss= 0.000065, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model905.ckpt Iter 906, Loss= 0.000065, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model906.ckpt Iter 907, Loss= 0.000065, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model907.ckpt Iter 908, Loss= 0.000065, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model908.ckpt Iter 909, Loss= 0.000065, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model909.ckpt Iter 910, Loss= 0.000064, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model910.ckpt Iter 911, Loss= 0.000064, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model911.ckpt Iter 912, Loss= 0.000064, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model912.ckpt Iter 913, Loss= 0.000064, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model913.ckpt Iter 914, Loss= 0.000064, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model914.ckpt Iter 915, Loss= 0.000063, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model915.ckpt Iter 916, Loss= 0.000063, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model916.ckpt Iter 917, Loss= 0.000063, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model917.ckpt Iter 918, Loss= 0.000063, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model918.ckpt Iter 919, Loss= 0.000063, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model919.ckpt Iter 920, Loss= 0.000063, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model920.ckpt Iter 921, Loss= 0.000062, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model921.ckpt Iter 922, Loss= 0.000062, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model922.ckpt Iter 923, Loss= 0.000062, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model923.ckpt Iter 924, Loss= 0.000062, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model924.ckpt Iter 925, Loss= 0.000062, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model925.ckpt Iter 926, Loss= 0.000062, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model926.ckpt Iter 927, Loss= 0.000061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model927.ckpt Iter 928, Loss= 0.000061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model928.ckpt Iter 929, Loss= 0.000061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model929.ckpt Iter 930, Loss= 0.000061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model930.ckpt Iter 931, Loss= 0.000061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model931.ckpt Iter 932, Loss= 0.000061, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model932.ckpt Iter 933, Loss= 0.000060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model933.ckpt Iter 934, Loss= 0.000060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model934.ckpt Iter 935, Loss= 0.000060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model935.ckpt Iter 936, Loss= 0.000060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model936.ckpt Iter 937, Loss= 0.000060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model937.ckpt Iter 938, Loss= 0.000060, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model938.ckpt Iter 939, Loss= 0.000059, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model939.ckpt Iter 940, Loss= 0.000059, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model940.ckpt Iter 941, Loss= 0.000059, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model941.ckpt Iter 942, Loss= 0.000059, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model942.ckpt Iter 943, Loss= 0.000059, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model943.ckpt Iter 944, Loss= 0.000059, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model944.ckpt Iter 945, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model945.ckpt Iter 946, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model946.ckpt Iter 947, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model947.ckpt Iter 948, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model948.ckpt Iter 949, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model949.ckpt Iter 950, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model950.ckpt Iter 951, Loss= 0.000058, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model951.ckpt Iter 952, Loss= 0.000057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model952.ckpt Iter 953, Loss= 0.000057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model953.ckpt Iter 954, Loss= 0.000057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model954.ckpt Iter 955, Loss= 0.000057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model955.ckpt Iter 956, Loss= 0.000057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model956.ckpt Iter 957, Loss= 0.000057, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model957.ckpt Iter 958, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model958.ckpt Iter 959, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model959.ckpt Iter 960, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model960.ckpt Iter 961, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model961.ckpt Iter 962, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model962.ckpt Iter 963, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model963.ckpt Iter 964, Loss= 0.000056, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model964.ckpt Iter 965, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model965.ckpt Iter 966, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model966.ckpt Iter 967, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model967.ckpt Iter 968, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model968.ckpt Iter 969, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model969.ckpt Iter 970, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model970.ckpt Iter 971, Loss= 0.000055, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model971.ckpt Iter 972, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model972.ckpt Iter 973, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model973.ckpt Iter 974, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model974.ckpt Iter 975, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model975.ckpt Iter 976, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model976.ckpt Iter 977, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model977.ckpt Iter 978, Loss= 0.000054, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model978.ckpt Iter 979, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model979.ckpt Iter 980, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model980.ckpt Iter 981, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model981.ckpt Iter 982, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model982.ckpt Iter 983, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model983.ckpt Iter 984, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model984.ckpt Iter 985, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model985.ckpt Iter 986, Loss= 0.000053, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model986.ckpt Iter 987, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model987.ckpt Iter 988, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model988.ckpt Iter 989, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model989.ckpt Iter 990, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model990.ckpt Iter 991, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model991.ckpt Iter 992, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model992.ckpt Iter 993, Loss= 0.000052, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model993.ckpt Iter 994, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model994.ckpt Iter 995, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model995.ckpt Iter 996, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model996.ckpt Iter 997, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model997.ckpt Iter 998, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model998.ckpt Iter 999, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model999.ckpt Iter 1000, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1000.ckpt Iter 1001, Loss= 0.000051, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1001.ckpt Iter 1002, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1002.ckpt Iter 1003, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1003.ckpt Iter 1004, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1004.ckpt Iter 1005, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1005.ckpt Iter 1006, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1006.ckpt Iter 1007, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1007.ckpt Iter 1008, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1008.ckpt Iter 1009, Loss= 0.000050, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1009.ckpt Iter 1010, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1010.ckpt Iter 1011, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1011.ckpt Iter 1012, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1012.ckpt Iter 1013, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1013.ckpt Iter 1014, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1014.ckpt Iter 1015, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1015.ckpt Iter 1016, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1016.ckpt Iter 1017, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1017.ckpt Iter 1018, Loss= 0.000049, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1018.ckpt Iter 1019, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1019.ckpt Iter 1020, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1020.ckpt Iter 1021, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1021.ckpt Iter 1022, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1022.ckpt Iter 1023, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1023.ckpt Iter 1024, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1024.ckpt Iter 1025, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1025.ckpt Iter 1026, Loss= 0.000048, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1026.ckpt Iter 1027, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1027.ckpt Iter 1028, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1028.ckpt Iter 1029, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1029.ckpt Iter 1030, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1030.ckpt Iter 1031, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1031.ckpt Iter 1032, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1032.ckpt Iter 1033, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1033.ckpt Iter 1034, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1034.ckpt Iter 1035, Loss= 0.000047, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1035.ckpt Iter 1036, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1036.ckpt Iter 1037, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1037.ckpt Iter 1038, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1038.ckpt Iter 1039, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1039.ckpt Iter 1040, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1040.ckpt Iter 1041, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1041.ckpt Iter 1042, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1042.ckpt Iter 1043, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1043.ckpt Iter 1044, Loss= 0.000046, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1044.ckpt Iter 1045, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1045.ckpt Iter 1046, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1046.ckpt Iter 1047, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1047.ckpt Iter 1048, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1048.ckpt Iter 1049, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1049.ckpt Iter 1050, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1050.ckpt Iter 1051, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1051.ckpt Iter 1052, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1052.ckpt Iter 1053, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1053.ckpt Iter 1054, Loss= 0.000045, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1054.ckpt Iter 1055, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1055.ckpt Iter 1056, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1056.ckpt Iter 1057, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1057.ckpt Iter 1058, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1058.ckpt Iter 1059, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1059.ckpt Iter 1060, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1060.ckpt Iter 1061, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1061.ckpt Iter 1062, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1062.ckpt Iter 1063, Loss= 0.000044, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1063.ckpt Iter 1064, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1064.ckpt Iter 1065, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1065.ckpt Iter 1066, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1066.ckpt Iter 1067, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1067.ckpt Iter 1068, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1068.ckpt Iter 1069, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1069.ckpt Iter 1070, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1070.ckpt Iter 1071, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1071.ckpt Iter 1072, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1072.ckpt Iter 1073, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1073.ckpt Iter 1074, Loss= 0.000043, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1074.ckpt Iter 1075, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1075.ckpt Iter 1076, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1076.ckpt Iter 1077, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1077.ckpt Iter 1078, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1078.ckpt Iter 1079, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1079.ckpt Iter 1080, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1080.ckpt Iter 1081, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1081.ckpt Iter 1082, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1082.ckpt Iter 1083, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1083.ckpt Iter 1084, Loss= 0.000042, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1084.ckpt Iter 1085, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1085.ckpt Iter 1086, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1086.ckpt Iter 1087, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1087.ckpt Iter 1088, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1088.ckpt Iter 1089, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1089.ckpt Iter 1090, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1090.ckpt Iter 1091, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1091.ckpt Iter 1092, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1092.ckpt Iter 1093, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1093.ckpt Iter 1094, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1094.ckpt Iter 1095, Loss= 0.000041, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1095.ckpt Iter 1096, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1096.ckpt Iter 1097, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1097.ckpt Iter 1098, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1098.ckpt Iter 1099, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1099.ckpt Iter 1100, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1100.ckpt Iter 1101, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1101.ckpt Iter 1102, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1102.ckpt Iter 1103, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1103.ckpt Iter 1104, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1104.ckpt Iter 1105, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1105.ckpt Iter 1106, Loss= 0.000040, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1106.ckpt Iter 1107, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1107.ckpt Iter 1108, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1108.ckpt Iter 1109, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1109.ckpt Iter 1110, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1110.ckpt Iter 1111, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1111.ckpt Iter 1112, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1112.ckpt Iter 1113, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1113.ckpt Iter 1114, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1114.ckpt Iter 1115, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1115.ckpt Iter 1116, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1116.ckpt Iter 1117, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1117.ckpt Iter 1118, Loss= 0.000039, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1118.ckpt Iter 1119, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1119.ckpt Iter 1120, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1120.ckpt Iter 1121, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1121.ckpt Iter 1122, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1122.ckpt Iter 1123, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1123.ckpt Iter 1124, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1124.ckpt Iter 1125, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1125.ckpt Iter 1126, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1126.ckpt Iter 1127, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1127.ckpt Iter 1128, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1128.ckpt Iter 1129, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1129.ckpt Iter 1130, Loss= 0.000038, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1130.ckpt Iter 1131, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1131.ckpt Iter 1132, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1132.ckpt Iter 1133, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1133.ckpt Iter 1134, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1134.ckpt Iter 1135, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1135.ckpt Iter 1136, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1136.ckpt Iter 1137, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1137.ckpt Iter 1138, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1138.ckpt Iter 1139, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1139.ckpt Iter 1140, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1140.ckpt Iter 1141, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1141.ckpt Iter 1142, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1142.ckpt Iter 1143, Loss= 0.000037, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1143.ckpt Iter 1144, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1144.ckpt Iter 1145, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1145.ckpt Iter 1146, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1146.ckpt Iter 1147, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1147.ckpt Iter 1148, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1148.ckpt Iter 1149, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1149.ckpt Iter 1150, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1150.ckpt Iter 1151, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1151.ckpt Iter 1152, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1152.ckpt Iter 1153, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1153.ckpt Iter 1154, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1154.ckpt Iter 1155, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1155.ckpt Iter 1156, Loss= 0.000036, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1156.ckpt Iter 1157, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1157.ckpt Iter 1158, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1158.ckpt Iter 1159, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1159.ckpt Iter 1160, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1160.ckpt Iter 1161, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1161.ckpt Iter 1162, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1162.ckpt Iter 1163, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1163.ckpt Iter 1164, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1164.ckpt Iter 1165, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1165.ckpt Iter 1166, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1166.ckpt Iter 1167, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1167.ckpt Iter 1168, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1168.ckpt Iter 1169, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1169.ckpt Iter 1170, Loss= 0.000035, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1170.ckpt Iter 1171, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1171.ckpt Iter 1172, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1172.ckpt Iter 1173, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1173.ckpt Iter 1174, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1174.ckpt Iter 1175, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1175.ckpt Iter 1176, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1176.ckpt Iter 1177, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1177.ckpt Iter 1178, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1178.ckpt Iter 1179, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1179.ckpt Iter 1180, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1180.ckpt Iter 1181, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1181.ckpt Iter 1182, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1182.ckpt Iter 1183, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1183.ckpt Iter 1184, Loss= 0.000034, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1184.ckpt Iter 1185, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1185.ckpt Iter 1186, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1186.ckpt Iter 1187, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1187.ckpt Iter 1188, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1188.ckpt Iter 1189, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1189.ckpt Iter 1190, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1190.ckpt Iter 1191, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1191.ckpt Iter 1192, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1192.ckpt Iter 1193, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1193.ckpt Iter 1194, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1194.ckpt Iter 1195, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1195.ckpt Iter 1196, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1196.ckpt Iter 1197, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1197.ckpt Iter 1198, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1198.ckpt Iter 1199, Loss= 0.000033, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1199.ckpt Iter 1200, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1200.ckpt Iter 1201, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1201.ckpt Iter 1202, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1202.ckpt Iter 1203, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1203.ckpt Iter 1204, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1204.ckpt Iter 1205, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1205.ckpt Iter 1206, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1206.ckpt Iter 1207, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1207.ckpt Iter 1208, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1208.ckpt Iter 1209, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1209.ckpt Iter 1210, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1210.ckpt Iter 1211, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1211.ckpt Iter 1212, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1212.ckpt Iter 1213, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1213.ckpt Iter 1214, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1214.ckpt Iter 1215, Loss= 0.000032, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1215.ckpt Iter 1216, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1216.ckpt Iter 1217, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1217.ckpt Iter 1218, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1218.ckpt Iter 1219, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1219.ckpt Iter 1220, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1220.ckpt Iter 1221, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1221.ckpt Iter 1222, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1222.ckpt Iter 1223, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1223.ckpt Iter 1224, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1224.ckpt Iter 1225, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1225.ckpt Iter 1226, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1226.ckpt Iter 1227, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1227.ckpt Iter 1228, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1228.ckpt Iter 1229, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1229.ckpt Iter 1230, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1230.ckpt Iter 1231, Loss= 0.000031, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1231.ckpt Iter 1232, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1232.ckpt Iter 1233, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1233.ckpt Iter 1234, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1234.ckpt Iter 1235, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1235.ckpt Iter 1236, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1236.ckpt Iter 1237, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1237.ckpt Iter 1238, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1238.ckpt Iter 1239, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1239.ckpt Iter 1240, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1240.ckpt Iter 1241, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1241.ckpt Iter 1242, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1242.ckpt Iter 1243, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1243.ckpt Iter 1244, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1244.ckpt Iter 1245, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1245.ckpt Iter 1246, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1246.ckpt Iter 1247, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1247.ckpt Iter 1248, Loss= 0.000030, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1248.ckpt Iter 1249, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1249.ckpt Iter 1250, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1250.ckpt Iter 1251, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1251.ckpt Iter 1252, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1252.ckpt Iter 1253, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1253.ckpt Iter 1254, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1254.ckpt Iter 1255, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1255.ckpt Iter 1256, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1256.ckpt Iter 1257, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1257.ckpt Iter 1258, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1258.ckpt Iter 1259, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1259.ckpt Iter 1260, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1260.ckpt Iter 1261, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1261.ckpt Iter 1262, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1262.ckpt Iter 1263, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1263.ckpt Iter 1264, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1264.ckpt Iter 1265, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1265.ckpt Iter 1266, Loss= 0.000029, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1266.ckpt Iter 1267, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1267.ckpt Iter 1268, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1268.ckpt Iter 1269, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1269.ckpt Iter 1270, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1270.ckpt Iter 1271, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1271.ckpt Iter 1272, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1272.ckpt Iter 1273, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1273.ckpt Iter 1274, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1274.ckpt Iter 1275, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1275.ckpt Iter 1276, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1276.ckpt Iter 1277, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1277.ckpt Iter 1278, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1278.ckpt Iter 1279, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1279.ckpt Iter 1280, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1280.ckpt Iter 1281, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1281.ckpt Iter 1282, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1282.ckpt Iter 1283, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1283.ckpt Iter 1284, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1284.ckpt Iter 1285, Loss= 0.000028, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1285.ckpt Iter 1286, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1286.ckpt Iter 1287, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1287.ckpt Iter 1288, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1288.ckpt Iter 1289, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1289.ckpt Iter 1290, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1290.ckpt Iter 1291, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1291.ckpt Iter 1292, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1292.ckpt Iter 1293, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1293.ckpt Iter 1294, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1294.ckpt Iter 1295, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1295.ckpt Iter 1296, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1296.ckpt Iter 1297, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1297.ckpt Iter 1298, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1298.ckpt Iter 1299, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1299.ckpt Iter 1300, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1300.ckpt Iter 1301, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1301.ckpt Iter 1302, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1302.ckpt Iter 1303, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1303.ckpt Iter 1304, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1304.ckpt Iter 1305, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1305.ckpt Iter 1306, Loss= 0.000027, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1306.ckpt Iter 1307, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1307.ckpt Iter 1308, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1308.ckpt Iter 1309, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1309.ckpt Iter 1310, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1310.ckpt Iter 1311, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1311.ckpt Iter 1312, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1312.ckpt Iter 1313, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1313.ckpt Iter 1314, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1314.ckpt Iter 1315, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1315.ckpt Iter 1316, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1316.ckpt Iter 1317, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1317.ckpt Iter 1318, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1318.ckpt Iter 1319, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1319.ckpt Iter 1320, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1320.ckpt Iter 1321, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1321.ckpt Iter 1322, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1322.ckpt Iter 1323, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1323.ckpt Iter 1324, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1324.ckpt Iter 1325, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1325.ckpt Iter 1326, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1326.ckpt Iter 1327, Loss= 0.000026, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1327.ckpt Iter 1328, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1328.ckpt Iter 1329, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1329.ckpt Iter 1330, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1330.ckpt Iter 1331, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1331.ckpt Iter 1332, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1332.ckpt Iter 1333, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1333.ckpt Iter 1334, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1334.ckpt Iter 1335, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1335.ckpt Iter 1336, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1336.ckpt Iter 1337, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1337.ckpt Iter 1338, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1338.ckpt Iter 1339, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1339.ckpt Iter 1340, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1340.ckpt Iter 1341, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1341.ckpt Iter 1342, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1342.ckpt Iter 1343, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1343.ckpt Iter 1344, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1344.ckpt Iter 1345, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1345.ckpt Iter 1346, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1346.ckpt Iter 1347, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1347.ckpt Iter 1348, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1348.ckpt Iter 1349, Loss= 0.000025, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1349.ckpt Iter 1350, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1350.ckpt Iter 1351, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1351.ckpt Iter 1352, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1352.ckpt Iter 1353, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1353.ckpt Iter 1354, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1354.ckpt Iter 1355, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1355.ckpt Iter 1356, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1356.ckpt Iter 1357, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1357.ckpt Iter 1358, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1358.ckpt Iter 1359, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1359.ckpt Iter 1360, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1360.ckpt Iter 1361, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1361.ckpt Iter 1362, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1362.ckpt Iter 1363, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1363.ckpt Iter 1364, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1364.ckpt Iter 1365, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1365.ckpt Iter 1366, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1366.ckpt Iter 1367, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1367.ckpt Iter 1368, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1368.ckpt Iter 1369, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1369.ckpt Iter 1370, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1370.ckpt Iter 1371, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1371.ckpt Iter 1372, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1372.ckpt Iter 1373, Loss= 0.000024, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1373.ckpt Iter 1374, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1374.ckpt Iter 1375, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1375.ckpt Iter 1376, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1376.ckpt Iter 1377, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1377.ckpt Iter 1378, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1378.ckpt Iter 1379, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1379.ckpt Iter 1380, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1380.ckpt Iter 1381, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1381.ckpt Iter 1382, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1382.ckpt Iter 1383, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1383.ckpt Iter 1384, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1384.ckpt Iter 1385, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1385.ckpt Iter 1386, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1386.ckpt Iter 1387, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1387.ckpt Iter 1388, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1388.ckpt Iter 1389, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1389.ckpt Iter 1390, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1390.ckpt Iter 1391, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1391.ckpt Iter 1392, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1392.ckpt Iter 1393, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1393.ckpt Iter 1394, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1394.ckpt Iter 1395, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1395.ckpt Iter 1396, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1396.ckpt Iter 1397, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1397.ckpt Iter 1398, Loss= 0.000023, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1398.ckpt Iter 1399, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1399.ckpt Iter 1400, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1400.ckpt Iter 1401, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1401.ckpt Iter 1402, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1402.ckpt Iter 1403, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1403.ckpt Iter 1404, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1404.ckpt Iter 1405, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1405.ckpt Iter 1406, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1406.ckpt Iter 1407, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1407.ckpt Iter 1408, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1408.ckpt Iter 1409, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1409.ckpt Iter 1410, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1410.ckpt Iter 1411, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1411.ckpt Iter 1412, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1412.ckpt Iter 1413, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1413.ckpt Iter 1414, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1414.ckpt Iter 1415, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1415.ckpt Iter 1416, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1416.ckpt Iter 1417, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1417.ckpt Iter 1418, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1418.ckpt Iter 1419, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1419.ckpt Iter 1420, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1420.ckpt Iter 1421, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1421.ckpt Iter 1422, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1422.ckpt Iter 1423, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1423.ckpt Iter 1424, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1424.ckpt Iter 1425, Loss= 0.000022, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1425.ckpt Iter 1426, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1426.ckpt Iter 1427, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1427.ckpt Iter 1428, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1428.ckpt Iter 1429, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1429.ckpt Iter 1430, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1430.ckpt Iter 1431, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1431.ckpt Iter 1432, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1432.ckpt Iter 1433, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1433.ckpt Iter 1434, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1434.ckpt Iter 1435, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1435.ckpt Iter 1436, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1436.ckpt Iter 1437, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1437.ckpt Iter 1438, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1438.ckpt Iter 1439, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1439.ckpt Iter 1440, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1440.ckpt Iter 1441, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1441.ckpt Iter 1442, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1442.ckpt Iter 1443, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1443.ckpt Iter 1444, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1444.ckpt Iter 1445, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1445.ckpt Iter 1446, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1446.ckpt Iter 1447, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1447.ckpt Iter 1448, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1448.ckpt Iter 1449, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1449.ckpt Iter 1450, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1450.ckpt Iter 1451, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1451.ckpt Iter 1452, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1452.ckpt Iter 1453, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1453.ckpt Iter 1454, Loss= 0.000021, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1454.ckpt Iter 1455, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1455.ckpt Iter 1456, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1456.ckpt Iter 1457, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1457.ckpt Iter 1458, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1458.ckpt Iter 1459, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1459.ckpt Iter 1460, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1460.ckpt Iter 1461, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1461.ckpt Iter 1462, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1462.ckpt Iter 1463, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1463.ckpt Iter 1464, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1464.ckpt Iter 1465, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1465.ckpt Iter 1466, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1466.ckpt Iter 1467, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1467.ckpt Iter 1468, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1468.ckpt Iter 1469, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1469.ckpt Iter 1470, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1470.ckpt Iter 1471, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1471.ckpt Iter 1472, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1472.ckpt Iter 1473, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1473.ckpt Iter 1474, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1474.ckpt Iter 1475, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1475.ckpt Iter 1476, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1476.ckpt Iter 1477, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1477.ckpt Iter 1478, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1478.ckpt Iter 1479, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1479.ckpt Iter 1480, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1480.ckpt Iter 1481, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1481.ckpt Iter 1482, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1482.ckpt Iter 1483, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1483.ckpt Iter 1484, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1484.ckpt Iter 1485, Loss= 0.000020, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1485.ckpt Iter 1486, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1486.ckpt Iter 1487, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1487.ckpt Iter 1488, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1488.ckpt Iter 1489, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1489.ckpt Iter 1490, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1490.ckpt Iter 1491, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1491.ckpt Iter 1492, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1492.ckpt Iter 1493, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1493.ckpt Iter 1494, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1494.ckpt Iter 1495, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1495.ckpt Iter 1496, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1496.ckpt Iter 1497, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1497.ckpt Iter 1498, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1498.ckpt Iter 1499, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1499.ckpt Iter 1500, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1500.ckpt Iter 1501, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1501.ckpt Iter 1502, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1502.ckpt Iter 1503, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1503.ckpt Iter 1504, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1504.ckpt Iter 1505, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1505.ckpt Iter 1506, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1506.ckpt Iter 1507, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1507.ckpt Iter 1508, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1508.ckpt Iter 1509, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1509.ckpt Iter 1510, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1510.ckpt Iter 1511, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1511.ckpt Iter 1512, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1512.ckpt Iter 1513, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1513.ckpt Iter 1514, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1514.ckpt Iter 1515, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1515.ckpt Iter 1516, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1516.ckpt Iter 1517, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1517.ckpt Iter 1518, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1518.ckpt Iter 1519, Loss= 0.000019, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1519.ckpt Iter 1520, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1520.ckpt Iter 1521, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1521.ckpt Iter 1522, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1522.ckpt Iter 1523, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1523.ckpt Iter 1524, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1524.ckpt Iter 1525, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1525.ckpt Iter 1526, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1526.ckpt Iter 1527, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1527.ckpt Iter 1528, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1528.ckpt Iter 1529, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1529.ckpt Iter 1530, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1530.ckpt Iter 1531, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1531.ckpt Iter 1532, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1532.ckpt Iter 1533, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1533.ckpt Iter 1534, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1534.ckpt Iter 1535, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1535.ckpt Iter 1536, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1536.ckpt Iter 1537, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1537.ckpt Iter 1538, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1538.ckpt Iter 1539, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1539.ckpt Iter 1540, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1540.ckpt Iter 1541, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1541.ckpt Iter 1542, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1542.ckpt Iter 1543, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1543.ckpt Iter 1544, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1544.ckpt Iter 1545, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1545.ckpt Iter 1546, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1546.ckpt Iter 1547, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1547.ckpt Iter 1548, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1548.ckpt Iter 1549, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1549.ckpt Iter 1550, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1550.ckpt Iter 1551, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1551.ckpt Iter 1552, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1552.ckpt Iter 1553, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1553.ckpt Iter 1554, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1554.ckpt Iter 1555, Loss= 0.000018, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1555.ckpt Iter 1556, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1556.ckpt Iter 1557, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1557.ckpt Iter 1558, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1558.ckpt Iter 1559, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1559.ckpt Iter 1560, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1560.ckpt Iter 1561, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1561.ckpt Iter 1562, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1562.ckpt Iter 1563, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1563.ckpt Iter 1564, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1564.ckpt Iter 1565, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1565.ckpt Iter 1566, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1566.ckpt Iter 1567, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1567.ckpt Iter 1568, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1568.ckpt Iter 1569, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1569.ckpt Iter 1570, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1570.ckpt Iter 1571, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1571.ckpt Iter 1572, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1572.ckpt Iter 1573, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1573.ckpt Iter 1574, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1574.ckpt Iter 1575, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1575.ckpt Iter 1576, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1576.ckpt Iter 1577, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1577.ckpt Iter 1578, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1578.ckpt Iter 1579, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1579.ckpt Iter 1580, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1580.ckpt Iter 1581, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1581.ckpt Iter 1582, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1582.ckpt Iter 1583, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1583.ckpt Iter 1584, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1584.ckpt Iter 1585, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1585.ckpt Iter 1586, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1586.ckpt Iter 1587, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1587.ckpt Iter 1588, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1588.ckpt Iter 1589, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1589.ckpt Iter 1590, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1590.ckpt Iter 1591, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1591.ckpt Iter 1592, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1592.ckpt Iter 1593, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1593.ckpt Iter 1594, Loss= 0.000017, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1594.ckpt Iter 1595, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1595.ckpt Iter 1596, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1596.ckpt Iter 1597, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1597.ckpt Iter 1598, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1598.ckpt Iter 1599, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1599.ckpt Iter 1600, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1600.ckpt Iter 1601, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1601.ckpt Iter 1602, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1602.ckpt Iter 1603, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1603.ckpt Iter 1604, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1604.ckpt Iter 1605, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1605.ckpt Iter 1606, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1606.ckpt Iter 1607, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1607.ckpt Iter 1608, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1608.ckpt Iter 1609, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1609.ckpt Iter 1610, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1610.ckpt Iter 1611, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1611.ckpt Iter 1612, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1612.ckpt Iter 1613, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1613.ckpt Iter 1614, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1614.ckpt Iter 1615, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1615.ckpt Iter 1616, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1616.ckpt Iter 1617, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1617.ckpt Iter 1618, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1618.ckpt Iter 1619, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1619.ckpt Iter 1620, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1620.ckpt Iter 1621, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1621.ckpt Iter 1622, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1622.ckpt Iter 1623, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1623.ckpt Iter 1624, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1624.ckpt Iter 1625, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1625.ckpt Iter 1626, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1626.ckpt Iter 1627, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1627.ckpt Iter 1628, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1628.ckpt Iter 1629, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1629.ckpt Iter 1630, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1630.ckpt Iter 1631, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1631.ckpt Iter 1632, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1632.ckpt Iter 1633, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1633.ckpt Iter 1634, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1634.ckpt Iter 1635, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1635.ckpt Iter 1636, Loss= 0.000016, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1636.ckpt Iter 1637, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1637.ckpt Iter 1638, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1638.ckpt Iter 1639, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1639.ckpt Iter 1640, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1640.ckpt Iter 1641, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1641.ckpt Iter 1642, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1642.ckpt Iter 1643, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1643.ckpt Iter 1644, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1644.ckpt Iter 1645, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1645.ckpt Iter 1646, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1646.ckpt Iter 1647, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1647.ckpt Iter 1648, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1648.ckpt Iter 1649, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1649.ckpt Iter 1650, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1650.ckpt Iter 1651, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1651.ckpt Iter 1652, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1652.ckpt Iter 1653, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1653.ckpt Iter 1654, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1654.ckpt Iter 1655, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1655.ckpt Iter 1656, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1656.ckpt Iter 1657, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1657.ckpt Iter 1658, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1658.ckpt Iter 1659, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1659.ckpt Iter 1660, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1660.ckpt Iter 1661, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1661.ckpt Iter 1662, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1662.ckpt Iter 1663, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1663.ckpt Iter 1664, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1664.ckpt Iter 1665, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1665.ckpt Iter 1666, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1666.ckpt Iter 1667, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1667.ckpt Iter 1668, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1668.ckpt Iter 1669, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1669.ckpt Iter 1670, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1670.ckpt Iter 1671, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1671.ckpt Iter 1672, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1672.ckpt Iter 1673, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1673.ckpt Iter 1674, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1674.ckpt Iter 1675, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1675.ckpt Iter 1676, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1676.ckpt Iter 1677, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1677.ckpt Iter 1678, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1678.ckpt Iter 1679, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1679.ckpt Iter 1680, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1680.ckpt Iter 1681, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1681.ckpt Iter 1682, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1682.ckpt Iter 1683, Loss= 0.000015, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1683.ckpt Iter 1684, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1684.ckpt Iter 1685, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1685.ckpt Iter 1686, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1686.ckpt Iter 1687, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1687.ckpt Iter 1688, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1688.ckpt Iter 1689, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1689.ckpt Iter 1690, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1690.ckpt Iter 1691, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1691.ckpt Iter 1692, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1692.ckpt Iter 1693, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1693.ckpt Iter 1694, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1694.ckpt Iter 1695, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1695.ckpt Iter 1696, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1696.ckpt Iter 1697, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1697.ckpt Iter 1698, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1698.ckpt Iter 1699, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1699.ckpt Iter 1700, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1700.ckpt Iter 1701, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1701.ckpt Iter 1702, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1702.ckpt Iter 1703, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1703.ckpt Iter 1704, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1704.ckpt Iter 1705, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1705.ckpt Iter 1706, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1706.ckpt Iter 1707, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1707.ckpt Iter 1708, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1708.ckpt Iter 1709, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1709.ckpt Iter 1710, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1710.ckpt Iter 1711, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1711.ckpt Iter 1712, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1712.ckpt Iter 1713, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1713.ckpt Iter 1714, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1714.ckpt Iter 1715, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1715.ckpt Iter 1716, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1716.ckpt Iter 1717, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1717.ckpt Iter 1718, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1718.ckpt Iter 1719, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1719.ckpt Iter 1720, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1720.ckpt Iter 1721, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1721.ckpt Iter 1722, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1722.ckpt Iter 1723, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1723.ckpt Iter 1724, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1724.ckpt Iter 1725, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1725.ckpt Iter 1726, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1726.ckpt Iter 1727, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1727.ckpt Iter 1728, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1728.ckpt Iter 1729, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1729.ckpt Iter 1730, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1730.ckpt Iter 1731, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1731.ckpt Iter 1732, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1732.ckpt Iter 1733, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1733.ckpt Iter 1734, Loss= 0.000014, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1734.ckpt Iter 1735, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1735.ckpt Iter 1736, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1736.ckpt Iter 1737, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1737.ckpt Iter 1738, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1738.ckpt Iter 1739, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1739.ckpt Iter 1740, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1740.ckpt Iter 1741, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1741.ckpt Iter 1742, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1742.ckpt Iter 1743, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1743.ckpt Iter 1744, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1744.ckpt Iter 1745, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1745.ckpt Iter 1746, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1746.ckpt Iter 1747, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1747.ckpt Iter 1748, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1748.ckpt Iter 1749, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1749.ckpt Iter 1750, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1750.ckpt Iter 1751, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1751.ckpt Iter 1752, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1752.ckpt Iter 1753, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1753.ckpt Iter 1754, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1754.ckpt Iter 1755, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1755.ckpt Iter 1756, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1756.ckpt Iter 1757, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1757.ckpt Iter 1758, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1758.ckpt Iter 1759, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1759.ckpt Iter 1760, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1760.ckpt Iter 1761, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1761.ckpt Iter 1762, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1762.ckpt Iter 1763, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1763.ckpt Iter 1764, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1764.ckpt Iter 1765, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1765.ckpt Iter 1766, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1766.ckpt Iter 1767, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1767.ckpt Iter 1768, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1768.ckpt Iter 1769, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1769.ckpt Iter 1770, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1770.ckpt Iter 1771, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1771.ckpt Iter 1772, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1772.ckpt Iter 1773, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1773.ckpt Iter 1774, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1774.ckpt Iter 1775, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1775.ckpt Iter 1776, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1776.ckpt Iter 1777, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1777.ckpt Iter 1778, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1778.ckpt Iter 1779, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1779.ckpt Iter 1780, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1780.ckpt Iter 1781, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1781.ckpt Iter 1782, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1782.ckpt Iter 1783, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1783.ckpt Iter 1784, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1784.ckpt Iter 1785, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1785.ckpt Iter 1786, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1786.ckpt Iter 1787, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1787.ckpt Iter 1788, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1788.ckpt Iter 1789, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1789.ckpt Iter 1790, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1790.ckpt Iter 1791, Loss= 0.000013, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1791.ckpt Iter 1792, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1792.ckpt Iter 1793, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1793.ckpt Iter 1794, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1794.ckpt Iter 1795, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1795.ckpt Iter 1796, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1796.ckpt Iter 1797, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1797.ckpt Iter 1798, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1798.ckpt Iter 1799, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1799.ckpt Iter 1800, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1800.ckpt Iter 1801, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1801.ckpt Iter 1802, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1802.ckpt Iter 1803, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1803.ckpt Iter 1804, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1804.ckpt Iter 1805, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1805.ckpt Iter 1806, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1806.ckpt Iter 1807, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1807.ckpt Iter 1808, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1808.ckpt Iter 1809, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1809.ckpt Iter 1810, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1810.ckpt Iter 1811, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1811.ckpt Iter 1812, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1812.ckpt Iter 1813, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1813.ckpt Iter 1814, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1814.ckpt Iter 1815, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1815.ckpt Iter 1816, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1816.ckpt Iter 1817, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1817.ckpt Iter 1818, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1818.ckpt Iter 1819, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1819.ckpt Iter 1820, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1820.ckpt Iter 1821, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1821.ckpt Iter 1822, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1822.ckpt Iter 1823, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1823.ckpt Iter 1824, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1824.ckpt Iter 1825, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1825.ckpt Iter 1826, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1826.ckpt Iter 1827, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1827.ckpt Iter 1828, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1828.ckpt Iter 1829, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1829.ckpt Iter 1830, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1830.ckpt Iter 1831, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1831.ckpt Iter 1832, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1832.ckpt Iter 1833, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1833.ckpt Iter 1834, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1834.ckpt Iter 1835, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1835.ckpt Iter 1836, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1836.ckpt Iter 1837, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1837.ckpt Iter 1838, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1838.ckpt Iter 1839, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1839.ckpt Iter 1840, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1840.ckpt Iter 1841, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1841.ckpt Iter 1842, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1842.ckpt Iter 1843, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1843.ckpt Iter 1844, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1844.ckpt Iter 1845, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1845.ckpt Iter 1846, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1846.ckpt Iter 1847, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1847.ckpt Iter 1848, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1848.ckpt Iter 1849, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1849.ckpt Iter 1850, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1850.ckpt Iter 1851, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1851.ckpt Iter 1852, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1852.ckpt Iter 1853, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1853.ckpt Iter 1854, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1854.ckpt Iter 1855, Loss= 0.000012, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1855.ckpt Iter 1856, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1856.ckpt Iter 1857, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1857.ckpt Iter 1858, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1858.ckpt Iter 1859, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1859.ckpt Iter 1860, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1860.ckpt Iter 1861, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1861.ckpt Iter 1862, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1862.ckpt Iter 1863, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1863.ckpt Iter 1864, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1864.ckpt Iter 1865, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1865.ckpt Iter 1866, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1866.ckpt Iter 1867, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1867.ckpt Iter 1868, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1868.ckpt Iter 1869, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1869.ckpt Iter 1870, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1870.ckpt Iter 1871, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1871.ckpt Iter 1872, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1872.ckpt Iter 1873, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1873.ckpt Iter 1874, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1874.ckpt Iter 1875, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1875.ckpt Iter 1876, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1876.ckpt Iter 1877, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1877.ckpt Iter 1878, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1878.ckpt Iter 1879, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1879.ckpt Iter 1880, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1880.ckpt Iter 1881, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1881.ckpt Iter 1882, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1882.ckpt Iter 1883, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1883.ckpt Iter 1884, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1884.ckpt Iter 1885, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1885.ckpt Iter 1886, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1886.ckpt Iter 1887, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1887.ckpt Iter 1888, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1888.ckpt Iter 1889, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1889.ckpt Iter 1890, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1890.ckpt Iter 1891, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1891.ckpt Iter 1892, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1892.ckpt Iter 1893, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1893.ckpt Iter 1894, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1894.ckpt Iter 1895, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1895.ckpt Iter 1896, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1896.ckpt Iter 1897, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1897.ckpt Iter 1898, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1898.ckpt Iter 1899, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1899.ckpt Iter 1900, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1900.ckpt Iter 1901, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1901.ckpt Iter 1902, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1902.ckpt Iter 1903, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1903.ckpt Iter 1904, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1904.ckpt Iter 1905, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1905.ckpt Iter 1906, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1906.ckpt Iter 1907, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1907.ckpt Iter 1908, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1908.ckpt Iter 1909, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1909.ckpt Iter 1910, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1910.ckpt Iter 1911, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1911.ckpt Iter 1912, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1912.ckpt Iter 1913, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1913.ckpt Iter 1914, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1914.ckpt Iter 1915, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1915.ckpt Iter 1916, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1916.ckpt Iter 1917, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1917.ckpt Iter 1918, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1918.ckpt Iter 1919, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1919.ckpt Iter 1920, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1920.ckpt Iter 1921, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1921.ckpt Iter 1922, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1922.ckpt Iter 1923, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1923.ckpt Iter 1924, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1924.ckpt Iter 1925, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1925.ckpt Iter 1926, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1926.ckpt Iter 1927, Loss= 0.000011, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1927.ckpt Iter 1928, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1928.ckpt Iter 1929, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1929.ckpt Iter 1930, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1930.ckpt Iter 1931, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1931.ckpt Iter 1932, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1932.ckpt Iter 1933, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1933.ckpt Iter 1934, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1934.ckpt Iter 1935, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1935.ckpt Iter 1936, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1936.ckpt Iter 1937, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1937.ckpt Iter 1938, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1938.ckpt Iter 1939, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1939.ckpt Iter 1940, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1940.ckpt Iter 1941, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1941.ckpt Iter 1942, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1942.ckpt Iter 1943, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1943.ckpt Iter 1944, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1944.ckpt Iter 1945, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1945.ckpt Iter 1946, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1946.ckpt Iter 1947, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1947.ckpt Iter 1948, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1948.ckpt Iter 1949, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1949.ckpt Iter 1950, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1950.ckpt Iter 1951, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1951.ckpt Iter 1952, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1952.ckpt Iter 1953, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1953.ckpt Iter 1954, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1954.ckpt Iter 1955, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1955.ckpt Iter 1956, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1956.ckpt Iter 1957, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1957.ckpt Iter 1958, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1958.ckpt Iter 1959, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1959.ckpt Iter 1960, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1960.ckpt Iter 1961, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1961.ckpt Iter 1962, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1962.ckpt Iter 1963, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1963.ckpt Iter 1964, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1964.ckpt Iter 1965, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1965.ckpt Iter 1966, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1966.ckpt Iter 1967, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1967.ckpt Iter 1968, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1968.ckpt Iter 1969, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1969.ckpt Iter 1970, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1970.ckpt Iter 1971, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1971.ckpt Iter 1972, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1972.ckpt Iter 1973, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1973.ckpt Iter 1974, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1974.ckpt Iter 1975, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1975.ckpt Iter 1976, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1976.ckpt Iter 1977, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1977.ckpt Iter 1978, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1978.ckpt Iter 1979, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1979.ckpt Iter 1980, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1980.ckpt Iter 1981, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1981.ckpt Iter 1982, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1982.ckpt Iter 1983, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1983.ckpt Iter 1984, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1984.ckpt Iter 1985, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1985.ckpt Iter 1986, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1986.ckpt Iter 1987, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1987.ckpt Iter 1988, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1988.ckpt Iter 1989, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1989.ckpt Iter 1990, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1990.ckpt Iter 1991, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1991.ckpt Iter 1992, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1992.ckpt Iter 1993, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1993.ckpt Iter 1994, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1994.ckpt Iter 1995, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1995.ckpt Iter 1996, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1996.ckpt Iter 1997, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1997.ckpt Iter 1998, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1998.ckpt Iter 1999, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1999.ckpt
plt.figure(figsize=(10,10))
plt.plot(range(len(train_loss)), train_loss, 'b', label='Training loss')
plt.plot(range(len(train_loss)), test_loss, 'r', label='Test loss')
plt.title('Training and Test loss')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.legend()
plt.show()
plt.figure(figsize=(10,10))
plt.plot(range(len(train_loss)), train_accuracy, 'b', label='Training Accuracy')
plt.plot(range(len(train_loss)), test_accuracy, 'r', label='Test Accuracy')
plt.title('Training and Test Accuracy')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Acurracy',fontsize=16)
plt.legend()
plt.show()
saver = tf.train.Saver()
with tf.variable_scope("", reuse = True):
with tf.Session() as sess:
saver.restore(sess, "/tmp/model1998.ckpt")
print("Model restored.")
#sess.run(init)
#print (tf.variable_scope.get_variable_scope())
w=tf.get_variable("W1")
w=w.read_value().eval()
#print(w.read_value().eval())
plt.subplot(421)
plt.subplots_adjust(bottom=0.1, right=4, top=4)
curr_img = np.reshape(w, (3,32))
plt.title ("iter 1 W1")
plt.imshow(curr_img, cmap='gray')
w1=tf.get_variable("W2")
w1=w1.read_value().eval()
plt.subplot(423)
curr_img = np.reshape(w1, (64,96))
plt.title ("iter 1 W2")
plt.imshow(curr_img, cmap='gray')
saver.restore(sess, "/tmp/model1999.ckpt")
print("Model restored.")
w1=tf.get_variable("W1")
w1=w1.read_value().eval()
plt.subplot(422)
curr_img = np.reshape(w1, (3,32))
plt.title ("iter 2 W1")
plt.imshow(curr_img, cmap='gray')
w1=tf.get_variable("W2")
w1=w1.read_value().eval()
plt.subplot(424)
curr_img = np.reshape(w1, (64,96))
plt.title ("iter 2 W2")
plt.imshow(curr_img, cmap='gray')
cax = plt.axes([0.1, 0.5, 0.75, 0.8])
plt.colorbar(cax=cax)
INFO:tensorflow:Restoring parameters from /tmp/model1998.ckpt Model restored. INFO:tensorflow:Restoring parameters from /tmp/model1999.ckpt Model restored.